{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "135e5f3c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>user_id</th>\n",
       "      <th>login_day</th>\n",
       "      <th>login_diff_time</th>\n",
       "      <th>distance_day</th>\n",
       "      <th>login_time</th>\n",
       "      <th>launch_time</th>\n",
       "      <th>chinese_subscribe_num</th>\n",
       "      <th>math_subscribe_num</th>\n",
       "      <th>add_friend</th>\n",
       "      <th>...</th>\n",
       "      <th>video_read</th>\n",
       "      <th>next_nize</th>\n",
       "      <th>answer_task</th>\n",
       "      <th>chapter_module</th>\n",
       "      <th>course_tab</th>\n",
       "      <th>slide_subscribe</th>\n",
       "      <th>baby_info</th>\n",
       "      <th>click_notunlocked</th>\n",
       "      <th>share</th>\n",
       "      <th>click_dialog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2000001563151338</td>\n",
       "      <td>5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>32</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2000001563163750</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2000001563266119</td>\n",
       "      <td>5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>22</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>34</td>\n",
       "      <td>42</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2000001566046975</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2000001566153564</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84</td>\n",
       "      <td>228</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4634</th>\n",
       "      <td>4634</td>\n",
       "      <td>2000002940317890</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11</td>\n",
       "      <td>271</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4635</th>\n",
       "      <td>4635</td>\n",
       "      <td>2000002941209916</td>\n",
       "      <td>2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>28</td>\n",
       "      <td>134</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4636</th>\n",
       "      <td>4636</td>\n",
       "      <td>2000002941212854</td>\n",
       "      <td>2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>23</td>\n",
       "      <td>161</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4637</th>\n",
       "      <td>4637</td>\n",
       "      <td>2000002943617210</td>\n",
       "      <td>2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>24</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4638</th>\n",
       "      <td>4638</td>\n",
       "      <td>2000002943810189</td>\n",
       "      <td>2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>25</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4639 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0           user_id  login_day  login_diff_time  distance_day  \\\n",
       "0              0  2000001563151338          5              1.4            32   \n",
       "1              1  2000001563163750          4              1.0            84   \n",
       "2              2  2000001563266119          5              0.8            22   \n",
       "3              3  2000001566046975          4              1.0            84   \n",
       "4              4  2000001566153564          1              0.0            84   \n",
       "...          ...               ...        ...              ...           ...   \n",
       "4634        4634  2000002940317890          2              1.0            11   \n",
       "4635        4635  2000002941209916          2              0.5            28   \n",
       "4636        4636  2000002941212854          2              0.5            23   \n",
       "4637        4637  2000002943617210          2              0.5            24   \n",
       "4638        4638  2000002943810189          2              0.5             1   \n",
       "\n",
       "      login_time  launch_time  chinese_subscribe_num  math_subscribe_num  \\\n",
       "0             16            1                      1                   1   \n",
       "1              0            0                      1                   0   \n",
       "2             10            0                      0                   0   \n",
       "3              0            0                      0                   0   \n",
       "4            228            0                      1                   0   \n",
       "...          ...          ...                    ...                 ...   \n",
       "4634         271            0                      1                   0   \n",
       "4635         134            1                      1                   0   \n",
       "4636         161            1                      1                   0   \n",
       "4637          47            0                      0                   0   \n",
       "4638          23            0                      0                   0   \n",
       "\n",
       "      add_friend  ...  video_read  next_nize  answer_task  chapter_module  \\\n",
       "0              1  ...           2          0            0               7   \n",
       "1              1  ...           0          0            0               0   \n",
       "2              1  ...          34         42           15               0   \n",
       "3              1  ...           0          0            0               0   \n",
       "4              1  ...          12          8            0              21   \n",
       "...          ...  ...         ...        ...          ...             ...   \n",
       "4634           1  ...          32         35            1               5   \n",
       "4635           1  ...           2          0            0              23   \n",
       "4636           1  ...          18         21            7               4   \n",
       "4637           1  ...          22         20            0               2   \n",
       "4638           1  ...          25         14            0               2   \n",
       "\n",
       "      course_tab  slide_subscribe  baby_info  click_notunlocked share  \\\n",
       "0              1                2          5                  0     0   \n",
       "1              0                0          0                  0     0   \n",
       "2              1                5          5                  0     1   \n",
       "3              0                0          0                  0     0   \n",
       "4              3                1          3                 14     0   \n",
       "...          ...              ...        ...                ...   ...   \n",
       "4634           0                2          0                  0     1   \n",
       "4635           2                2          0                  0     1   \n",
       "4636          13                1          0                  3     6   \n",
       "4637           0                0          5                  0     4   \n",
       "4638           0                0          0                  0     5   \n",
       "\n",
       "      click_dialog  \n",
       "0                1  \n",
       "1                0  \n",
       "2                4  \n",
       "3                0  \n",
       "4                0  \n",
       "...            ...  \n",
       "4634             1  \n",
       "4635             1  \n",
       "4636             0  \n",
       "4637             1  \n",
       "4638             0  \n",
       "\n",
       "[4639 rows x 50 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "user1 = pd.read_csv(r\"C:\\Users\\阿璃\\Desktop\\数据分析大作业项目题目（三选一）\\B题\\处理好的数据\\login_day1.csv\")\n",
    "user3 = pd.read_csv(r\"C:\\Users\\阿璃\\Desktop\\数据分析大作业项目题目（三选一）\\B题\\处理好的数据\\user_info1.csv\")\n",
    "user = pd.read_csv(r\"C:\\Users\\阿璃\\Desktop\\数据分析大作业项目题目（三选一）\\B题\\处理好的数据\\user.csv\")\n",
    "user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "98453ced",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>城市</th>\n",
       "      <th>数量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>重庆</td>\n",
       "      <td>40620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>成都</td>\n",
       "      <td>3604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>运城</td>\n",
       "      <td>3540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>广州</td>\n",
       "      <td>3184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>北京</td>\n",
       "      <td>2589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>五指山</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>定安县</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>阿拉尔</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>黄南藏族自治州</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>林芝</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>362 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          城市     数量\n",
       "0         重庆  40620\n",
       "1         成都   3604\n",
       "2         运城   3540\n",
       "3         广州   3184\n",
       "4         北京   2589\n",
       "..       ...    ...\n",
       "357      五指山      2\n",
       "358      定安县      2\n",
       "359      阿拉尔      2\n",
       "360  黄南藏族自治州      1\n",
       "361       林芝      1\n",
       "\n",
       "[362 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_count = user3['city_num'].value_counts() # 统计城市用户数量\n",
    "\n",
    "df = pd.DataFrame({'城市': city_count.index, '数量': city_count.values})  \n",
    "\n",
    "df.to_csv('city_count.csv', header=True, index_label='序号')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c4a0f492",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['重庆', 40620],\n",
       " ['成都', 3604],\n",
       " ['运城', 3540],\n",
       " ['广州', 3184],\n",
       " ['北京', 2589],\n",
       " ['洛阳', 2444],\n",
       " ['保定', 2160],\n",
       " ['泉州', 1920],\n",
       " ['深圳', 1887],\n",
       " ['上海', 1662],\n",
       " ['郑州', 1653],\n",
       " ['西安', 1646],\n",
       " ['邯郸', 1606],\n",
       " ['东莞', 1492],\n",
       " ['衡阳', 1295],\n",
       " ['福州', 1287],\n",
       " ['贵阳', 1154],\n",
       " ['杭州', 1127],\n",
       " ['三门峡', 1105],\n",
       " ['佛山', 1100],\n",
       " ['石家庄', 1010],\n",
       " ['天津', 930],\n",
       " ['济宁', 914],\n",
       " ['南昌', 892],\n",
       " ['临汾', 877],\n",
       " ['邢台', 865],\n",
       " ['长沙', 855],\n",
       " ['苏州', 834],\n",
       " ['咸阳', 831],\n",
       " ['济南', 798],\n",
       " ['青岛', 783],\n",
       " ['徐州', 739],\n",
       " ['合肥', 732],\n",
       " ['晋城', 728],\n",
       " ['武汉', 722],\n",
       " ['唐山', 678],\n",
       " ['临沂', 651],\n",
       " ['中山', 646],\n",
       " ['南京', 641],\n",
       " ['德州', 616],\n",
       " ['潍坊', 588],\n",
       " ['沧州', 574],\n",
       " ['惠州', 572],\n",
       " ['沈阳', 571],\n",
       " ['庆阳', 533],\n",
       " ['太原', 523],\n",
       " ['厦门', 508],\n",
       " ['温州', 464],\n",
       " ['烟台', 440],\n",
       " ['驻马店', 437],\n",
       " ['衡水', 431],\n",
       " ['哈尔滨', 418],\n",
       " ['菏泽', 416],\n",
       " ['乌鲁木齐', 412],\n",
       " ['error', 412],\n",
       " ['资阳', 410],\n",
       " ['廊坊', 402],\n",
       " ['长春', 397],\n",
       " ['无锡', 393],\n",
       " ['长治', 367],\n",
       " ['泰安', 361],\n",
       " ['濮阳', 355],\n",
       " ['大连', 349],\n",
       " ['宁波', 343],\n",
       " ['南宁', 341],\n",
       " ['阜阳', 316],\n",
       " ['南阳', 315],\n",
       " ['呼和浩特', 309],\n",
       " ['开封', 307],\n",
       " ['银川', 306],\n",
       " ['榆林', 305],\n",
       " ['聊城', 305],\n",
       " ['金华', 303],\n",
       " ['常州', 289],\n",
       " ['许昌', 288],\n",
       " ['南通', 286],\n",
       " ['昆明', 284],\n",
       " ['赣州', 282],\n",
       " ['汕头', 281],\n",
       " ['达州', 280],\n",
       " ['南充', 278],\n",
       " ['新乡', 274],\n",
       " ['渭南', 273],\n",
       " ['张家口', 271],\n",
       " ['湛江', 270],\n",
       " ['淄博', 266],\n",
       " ['台州', 262],\n",
       " ['吕梁', 260],\n",
       " ['商丘', 255],\n",
       " ['赤峰', 255],\n",
       " ['安阳', 254],\n",
       " ['周口', 253],\n",
       " ['连云港', 251],\n",
       " ['盐城', 250],\n",
       " ['梅州', 248],\n",
       " ['包头', 244],\n",
       " ['枣庄', 241],\n",
       " ['滨州', 240],\n",
       " ['江门', 239],\n",
       " ['晋中', 237],\n",
       " ['漳州', 235],\n",
       " ['肇庆', 234],\n",
       " ['遵义', 233],\n",
       " ['鄂尔多斯', 231],\n",
       " ['日照', 227],\n",
       " ['岳阳', 227],\n",
       " ['株洲', 223],\n",
       " ['九江', 221],\n",
       " ['绍兴', 219],\n",
       " ['兰州', 217],\n",
       " ['铁岭', 214],\n",
       " ['东营', 214],\n",
       " ['桂林', 213],\n",
       " ['亳州', 213],\n",
       " ['襄阳', 212],\n",
       " ['扬州', 211],\n",
       " ['宿迁', 208],\n",
       " ['威海', 207],\n",
       " ['泰州', 203],\n",
       " ['莆田', 201],\n",
       " ['营口', 198],\n",
       " ['揭阳', 197],\n",
       " ['六安', 195],\n",
       " ['铜仁', 194],\n",
       " ['秦皇岛', 194],\n",
       " ['邵阳', 192],\n",
       " ['平顶山', 191],\n",
       " ['忻州', 191],\n",
       " ['淮安', 189],\n",
       " ['茂名', 189],\n",
       " ['珠海', 185],\n",
       " ['张家界', 183],\n",
       " ['信阳', 178],\n",
       " ['大同', 177],\n",
       " ['龙岩', 177],\n",
       " ['孝感', 173],\n",
       " ['黄冈', 172],\n",
       " ['朝阳', 171],\n",
       " ['嘉兴', 171],\n",
       " ['上饶', 170],\n",
       " ['郴州', 168],\n",
       " ['海口', 165],\n",
       " ['宜春', 164],\n",
       " ['玉林', 158],\n",
       " ['娄底', 155],\n",
       " ['宁德', 154],\n",
       " ['永州', 152],\n",
       " ['益阳', 150],\n",
       " ['柳州', 147],\n",
       " ['伊犁哈萨克自治州', 146],\n",
       " ['绵阳', 145],\n",
       " ['怀化', 144],\n",
       " ['焦作', 143],\n",
       " ['宝鸡', 142],\n",
       " ['常德', 142],\n",
       " ['镇江', 141],\n",
       " ['锦州', 140],\n",
       " ['巴彦淖尔', 137],\n",
       " ['咸宁', 136],\n",
       " ['昌吉回族自治州', 134],\n",
       " ['承德', 134],\n",
       " ['荆州', 133],\n",
       " ['朔州', 131],\n",
       " ['宜昌', 130],\n",
       " ['安庆', 129],\n",
       " ['黔南布依族苗族自治州', 128],\n",
       " ['平凉', 127],\n",
       " ['清远', 127],\n",
       " ['韶关', 124],\n",
       " ['十堰', 121],\n",
       " ['萍乡', 121],\n",
       " ['巴音郭楞蒙古自治州', 120],\n",
       " ['大庆', 120],\n",
       " ['西宁', 118],\n",
       " ['延安', 117],\n",
       " ['河源', 116],\n",
       " ['吉林市', 115],\n",
       " ['贵港', 115],\n",
       " ['蚌埠', 113],\n",
       " ['白城', 112],\n",
       " ['商洛', 110],\n",
       " ['抚州', 110],\n",
       " ['泸州', 109],\n",
       " ['吉安', 109],\n",
       " ['宿州', 108],\n",
       " ['三明', 108],\n",
       " ['淮南', 105],\n",
       " ['广安', 105],\n",
       " ['曲靖', 103],\n",
       " ['黄石', 101],\n",
       " ['云浮', 100],\n",
       " ['芜湖', 100],\n",
       " ['呼伦贝尔', 99],\n",
       " ['潮州', 99],\n",
       " ['漯河', 98],\n",
       " ['天水', 98],\n",
       " ['鞍山', 97],\n",
       " ['白银', 97],\n",
       " ['齐齐哈尔', 96],\n",
       " ['葫芦岛', 96],\n",
       " ['德阳', 96],\n",
       " ['湖州', 96],\n",
       " ['南平', 95],\n",
       " ['湘潭', 93],\n",
       " ['遂宁', 92],\n",
       " ['宜宾', 92],\n",
       " ['汕尾', 89],\n",
       " ['宣城', 88],\n",
       " ['阳江', 88],\n",
       " ['钦州', 87],\n",
       " ['汉中', 87],\n",
       " ['延边朝鲜族自治州', 85],\n",
       " ['北海', 84],\n",
       " ['通辽', 82],\n",
       " ['毕节', 82],\n",
       " ['衢州', 82],\n",
       " ['河池', 81],\n",
       " ['荆门', 80],\n",
       " ['淮北', 80],\n",
       " ['内江', 78],\n",
       " ['莱芜', 78],\n",
       " ['佳木斯', 77],\n",
       " ['乌兰察布', 76],\n",
       " ['鄂州', 76],\n",
       " ['通化', 76],\n",
       " ['牡丹江', 74],\n",
       " ['梧州', 74],\n",
       " ['眉山', 74],\n",
       " ['乐山', 72],\n",
       " ['阳泉', 72],\n",
       " ['滁州', 72],\n",
       " ['三亚', 71],\n",
       " ['喀什地区', 71],\n",
       " ['黄山', 70],\n",
       " ['抚顺', 70],\n",
       " ['安康', 69],\n",
       " ['景德镇', 68],\n",
       " ['雅安', 68],\n",
       " ['塔城地区', 68],\n",
       " ['定西', 67],\n",
       " ['百色', 67],\n",
       " ['湘西土家族苗族自治州', 67],\n",
       " ['红河哈尼族彝族自治州', 66],\n",
       " ['酒泉', 66],\n",
       " ['盘锦', 65],\n",
       " ['玉溪', 64],\n",
       " ['随州', 63],\n",
       " ['恩施土家族苗族自治州', 63],\n",
       " ['凉山彝族自治州', 62],\n",
       " ['阿克苏地区', 61],\n",
       " ['丹东', 61],\n",
       " ['舟山', 61],\n",
       " ['巴中', 59],\n",
       " ['张掖', 59],\n",
       " ['松原', 59],\n",
       " ['鹤壁', 57],\n",
       " ['阜新', 56],\n",
       " ['文山壮族苗族自治州', 55],\n",
       " ['绥化', 55],\n",
       " ['崇左', 55],\n",
       " ['新余', 54],\n",
       " ['鸡西', 54],\n",
       " ['马鞍山', 53],\n",
       " ['武威', 53],\n",
       " ['锡林郭勒盟', 52],\n",
       " ['六盘水', 50],\n",
       " ['济源', 49],\n",
       " ['陇南', 48],\n",
       " ['双鸭山', 48],\n",
       " ['铜陵', 48],\n",
       " ['兴安盟', 48],\n",
       " ['昭通', 47],\n",
       " ['黑河', 47],\n",
       " ['仙桃', 46],\n",
       " ['吴忠', 46],\n",
       " ['克拉玛依', 45],\n",
       " ['广元', 45],\n",
       " ['乌海', 45],\n",
       " ['辽阳', 45],\n",
       " ['中卫', 45],\n",
       " ['本溪', 44],\n",
       " ['大理白族自治州', 44],\n",
       " ['石河子', 43],\n",
       " ['楚雄彝族自治州', 43],\n",
       " ['池州', 43],\n",
       " ['自贡', 43],\n",
       " ['石嘴山', 42],\n",
       " ['来宾', 42],\n",
       " ['临夏回族自治州', 41],\n",
       " ['白山', 41],\n",
       " ['黔西南布依族苗族自治州', 41],\n",
       " ['四平', 40],\n",
       " ['贺州', 40],\n",
       " ['安顺', 39],\n",
       " ['黔东南苗族侗族自治州', 38],\n",
       " ['哈密', 37],\n",
       " ['固原', 36],\n",
       " ['丽水', 35],\n",
       " ['鹤岗', 34],\n",
       " ['甘孜藏族自治州', 34],\n",
       " ['天门', 33],\n",
       " ['阿勒泰地区', 32],\n",
       " ['博尔塔拉蒙古自治州', 30],\n",
       " ['和田地区', 30],\n",
       " ['普洱', 28],\n",
       " ['防城港', 28],\n",
       " ['伊春', 28],\n",
       " ['西双版纳傣族自治州', 27],\n",
       " ['攀枝花', 27],\n",
       " ['澄迈县', 26],\n",
       " ['拉萨', 26],\n",
       " ['保山', 25],\n",
       " ['陵水黎族自治县', 25],\n",
       " ['临沧', 24],\n",
       " ['丽江', 24],\n",
       " ['吐鲁番', 24],\n",
       " ['琼海', 23],\n",
       " ['克孜勒苏柯尔克孜自治州', 23],\n",
       " ['阿坝藏族羌族自治州', 22],\n",
       " ['阿拉善盟', 22],\n",
       " ['鹰潭', 22],\n",
       " ['铜川', 21],\n",
       " ['德宏傣族景颇族自治州', 21],\n",
       " ['海东', 20],\n",
       " ['潜江', 19],\n",
       " ['辽源', 17],\n",
       " ['文昌', 16],\n",
       " ['甘南藏族自治州', 15],\n",
       " ['海西蒙古族藏族自治州', 14],\n",
       " ['嘉峪关', 13],\n",
       " ['万宁', 13],\n",
       " ['海南藏族自治州', 12],\n",
       " ['大兴安岭地区', 12],\n",
       " ['北屯', 11],\n",
       " ['金昌', 11],\n",
       " ['七台河', 9],\n",
       " ['五家渠', 8],\n",
       " ['迪庆藏族自治州', 8],\n",
       " ['图木舒克', 7],\n",
       " ['海北藏族自治州', 7],\n",
       " ['儋州', 6],\n",
       " ['琼中黎族苗族自治县', 6],\n",
       " ['怒江傈僳族自治州', 5],\n",
       " ['日喀则', 5],\n",
       " ['山南', 5],\n",
       " ['乐东黎族自治县', 4],\n",
       " ['昌都', 4],\n",
       " ['昌江黎族自治县', 4],\n",
       " ['新北市', 4],\n",
       " ['那曲', 3],\n",
       " ['玉树藏族自治州', 3],\n",
       " ['临高县', 3],\n",
       " ['东方', 3],\n",
       " ['白沙黎族自治县', 3],\n",
       " ['保亭黎族苗族自治县', 3],\n",
       " ['台北市', 2],\n",
       " ['屯昌县', 2],\n",
       " ['五指山', 2],\n",
       " ['定安县', 2],\n",
       " ['阿拉尔', 2],\n",
       " ['黄南藏族自治州', 1],\n",
       " ['林芝', 1]]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df.loc[:, '城市':].apply(list, axis=1).to_list() #转化为列表类型\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8864e5bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pyecharts.charts import Map\n",
    "from pyecharts import options as opts\n",
    "\n",
    "city_count = user3['city_num'].value_counts() # 统计城市用户数量\n",
    "\n",
    "df = pd.DataFrame({'城市': city_count.index, '数量': city_count.values})  \n",
    "\n",
    "df.to_csv('city_count.csv', header=True, index_label='序号')\n",
    "\n",
    "# 发现数据城市名与地图城市名有些不匹配，所以遍历数据中的城市，添加\"市\"字\n",
    "for i in range(len(df)):\n",
    "    if 2 <= len(df.loc[i, '城市']) <= 4:  # 判断城市名称长度是否为2到4\n",
    "        df.loc[i, '城市'] = df.loc[i, '城市'] + \"市\"\n",
    "\n",
    "# 保存数据\n",
    "df.to_csv(\"data.csv\", index=False)\n",
    "\n",
    "data = df.loc[:, '城市':].apply(list, axis=1).to_list()\n",
    "\n",
    "#代码使用Map类创建了一个地图对象c，并通过.add()方法将数据添加到地图中。\n",
    "#地图的类型是\"china-cities\"，标签不显示，地图符号不显示。\n",
    "#通过.set_global_opts()方法设置了视觉映射的最小值和最大值，图例显示，标题为\"用户城市分布地图\"。最后，通过.render()方法将地图保存为名为\"map_china_cities.html\"的HTML文件。\n",
    "c = (\n",
    "    Map(init_opts=opts.InitOpts(width='1100px', height='1000px'))\n",
    "    .add(\n",
    "        '城市',\n",
    "        data,\n",
    "        \"china-cities\",\n",
    "        label_opts=opts.LabelOpts(is_show=False),\n",
    "        is_map_symbol_show=False,\n",
    "    )\n",
    "    .set_global_opts(\n",
    "        visualmap_opts=opts.VisualMapOpts(min_=0,\n",
    "                                          max_=1000,\n",
    "                                          is_show=False),\n",
    "        legend_opts=opts.LegendOpts(is_show=False),\n",
    "        title_opts=opts.TitleOpts(title=\"用户城市分布地图\")\n",
    "    )\n",
    "     .render(\"map_china_cities.html\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "219496d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>user_id</th>\n",
       "      <th>login_day</th>\n",
       "      <th>login_diff_time</th>\n",
       "      <th>distance_day</th>\n",
       "      <th>login_time</th>\n",
       "      <th>launch_time</th>\n",
       "      <th>chinese_subscribe_num</th>\n",
       "      <th>math_subscribe_num</th>\n",
       "      <th>add_friend</th>\n",
       "      <th>add_group</th>\n",
       "      <th>camp_num</th>\n",
       "      <th>learn_num</th>\n",
       "      <th>finish_num</th>\n",
       "      <th>study_num</th>\n",
       "      <th>coupon</th>\n",
       "      <th>course_order_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2000001555945280</td>\n",
       "      <td>7</td>\n",
       "      <td>6.86</td>\n",
       "      <td>131</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2000001556645228</td>\n",
       "      <td>4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>81</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2000001558047804</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>179</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2000001558146467</td>\n",
       "      <td>6</td>\n",
       "      <td>1.00</td>\n",
       "      <td>32</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2000001558146878</td>\n",
       "      <td>4</td>\n",
       "      <td>1.75</td>\n",
       "      <td>361</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135612</th>\n",
       "      <td>135612</td>\n",
       "      <td>2000002947317726</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135613</th>\n",
       "      <td>135613</td>\n",
       "      <td>2000002947317758</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135614</th>\n",
       "      <td>135614</td>\n",
       "      <td>2000002947317827</td>\n",
       "      <td>4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135615</th>\n",
       "      <td>135615</td>\n",
       "      <td>2000002947317941</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>393</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135616</th>\n",
       "      <td>135616</td>\n",
       "      <td>2000002948014779</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135617 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0           user_id  login_day  login_diff_time  \\\n",
       "0                0  2000001555945280          7             6.86   \n",
       "1                1  2000001556645228          4             1.00   \n",
       "2                2  2000001558047804          1             0.00   \n",
       "3                3  2000001558146467          6             1.00   \n",
       "4                4  2000001558146878          4             1.75   \n",
       "...            ...               ...        ...              ...   \n",
       "135612      135612  2000002947317726          1             0.00   \n",
       "135613      135613  2000002947317758          1             0.00   \n",
       "135614      135614  2000002947317827          4             1.00   \n",
       "135615      135615  2000002947317941          1             0.00   \n",
       "135616      135616  2000002948014779          1             0.00   \n",
       "\n",
       "        distance_day  login_time  launch_time  chinese_subscribe_num  \\\n",
       "0                131           1            1                      1   \n",
       "1                 81           3            1                      1   \n",
       "2                179           3            0                      1   \n",
       "3                 32          24            3                      0   \n",
       "4                361          39            0                      0   \n",
       "...              ...         ...          ...                    ...   \n",
       "135612             0           2            0                      0   \n",
       "135613             0           2            0                      0   \n",
       "135614            84           0            0                      0   \n",
       "135615             0         393            0                      0   \n",
       "135616             0           4            0                      0   \n",
       "\n",
       "        math_subscribe_num  add_friend  add_group  camp_num  learn_num  \\\n",
       "0                        0           1          1         0          0   \n",
       "1                        1           1          1         2          1   \n",
       "2                        0           1          1         2          0   \n",
       "3                        0           1          1         1          5   \n",
       "4                        1           1          1         2          0   \n",
       "...                    ...         ...        ...       ...        ...   \n",
       "135612                   0           1          1         1          0   \n",
       "135613                   0           1          1         1          0   \n",
       "135614                   0           1          1         1          0   \n",
       "135615                   0           1          1         1          0   \n",
       "135616                   0           1          1         2          0   \n",
       "\n",
       "        finish_num  study_num  coupon  course_order_num  \n",
       "0                0          0       0                 4  \n",
       "1                0          0       0                 0  \n",
       "2                0          0       0                 0  \n",
       "3                5          0       0                 1  \n",
       "4                0          1       0                 0  \n",
       "...            ...        ...     ...               ...  \n",
       "135612           0          0       0                 0  \n",
       "135613           0          0       0                 0  \n",
       "135614           0          0       0                 0  \n",
       "135615           0          0       0                 0  \n",
       "135616           0          0       0                 0  \n",
       "\n",
       "[135617 rows x 17 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user1 = pd.read_csv(r\"C:\\Users\\阿璃\\Desktop\\数据分析大作业项目题目（三选一）\\B题\\处理好的数据\\login_day1.csv\")\n",
    "user1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "31ba7ab8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>登录天数</th>\n",
       "      <th>登录用户数量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>24235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>21229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>19865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>18938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>17069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>14805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>12476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>6212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>29</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>39</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>101</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>91</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    登录天数  登录用户数量\n",
       "0      4   24235\n",
       "1      5   21229\n",
       "2      6   19865\n",
       "3      3   18938\n",
       "4      2   17069\n",
       "5      7   14805\n",
       "6      1   12476\n",
       "7      8    6212\n",
       "8      9     481\n",
       "9     10     129\n",
       "10    11      50\n",
       "11    12      31\n",
       "12    14      12\n",
       "13    13      11\n",
       "14    17       8\n",
       "15    16       8\n",
       "16    20       5\n",
       "17    15       5\n",
       "18    19       4\n",
       "19    30       3\n",
       "20    29       3\n",
       "21    24       3\n",
       "22    28       3\n",
       "23    18       3\n",
       "24    23       2\n",
       "25    39       2\n",
       "26    36       2\n",
       "27    33       2\n",
       "28    43       1\n",
       "29    68       1\n",
       "30    56       1\n",
       "31    38       1\n",
       "32    21       1\n",
       "33    35       1\n",
       "34    34       1\n",
       "35    53       1\n",
       "36    41       1\n",
       "37   101       1\n",
       "38    55       1\n",
       "39    84       1\n",
       "40    91       1\n",
       "41   102       1\n",
       "42   108       1\n",
       "43    72       1\n",
       "44    82       1\n",
       "45    22       1\n",
       "46    70       1\n",
       "47    63       1\n",
       "48    32       1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#，登录天数，登录间隔\n",
    "\n",
    "user_day= user1['login_day'].value_counts() # 统计用户登录天数\n",
    "\n",
    "df1 = pd.DataFrame({'登录天数': user_day.index, '登录用户数量': user_day.values})  \n",
    "\n",
    "df1.to_csv('login_day.csv', header=True, index_label='序号')\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bedd417b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.rcParams[\"font.sans-serif\"] = \"YouYuan\"\n",
    "plt.rcParams[\"axes.unicode_minus\"] = False\n",
    "plt.figure(figsize=(12,12),dpi = 100)\n",
    "\n",
    "plt.bar(df1['登录天数'],df1['登录用户数量'])\n",
    "plt.xlabel('登录天数')\n",
    "plt.ylabel('登录用户数量')\n",
    "plt.title('登录天数分析图')\n",
    "\n",
    "\n",
    "plt.xticks(df.index[:100:3], rotation=45)\n",
    "\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e64f7d9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制饼图\n",
    "plt.pie(df1['登录用户数量'], labels=df1['登录天数'], autopct='%1.1f%%')\n",
    "plt.title('登录天数占比')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "78100d7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>登录间隔时长</th>\n",
       "      <th>登录间隔时长用户数量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.00</td>\n",
       "      <td>34023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.00</td>\n",
       "      <td>12476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.50</td>\n",
       "      <td>7604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.83</td>\n",
       "      <td>6966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.80</td>\n",
       "      <td>6863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>7.79</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>23.86</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>25.17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>32.33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>2.85</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>661 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     登录间隔时长  登录间隔时长用户数量\n",
       "0      1.00       34023\n",
       "1      0.00       12476\n",
       "2      0.50        7604\n",
       "3      0.83        6966\n",
       "4      0.80        6863\n",
       "..      ...         ...\n",
       "656    7.79           1\n",
       "657   23.86           1\n",
       "658   25.17           1\n",
       "659   32.33           1\n",
       "660    2.85           1\n",
       "\n",
       "[661 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_time= user1['login_diff_time'].value_counts() # 统计用户登录间隔\n",
    "\n",
    "df2 = pd.DataFrame({'登录间隔时长': user_time.index, '登录间隔时长用户数量': user_time.values})  \n",
    "\n",
    "df2.to_csv('login_time.csv', header=True, index_label='序号')\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3d00812b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2000x1600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"font.sans-serif\"] = \"YouYuan\"\n",
    "plt.rcParams[\"axes.unicode_minus\"] = False\n",
    "plt.figure(figsize=(10,8),dpi = 200)\n",
    "\n",
    "plt.bar(df2['登录间隔时长'],df2['登录间隔时长用户数量'])\n",
    "plt.xlabel('登录间隔时长')\n",
    "plt.ylabel('登录间隔用户数量')\n",
    "plt.title('登录间隔时长分析图')\n",
    "\n",
    "\n",
    "plt.xticks(df.index[:100:5], rotation=45)\n",
    "\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "92f4918a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAGbCAYAAADnZrZIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAADQrElEQVR4nOydd3gc1dWH3ylb1XuxLblXuRvbgAFjwDgBTCAUA4k/ICSBQEIIAUISCB1CCCaEDqGXEIoBA6YYDBjci+Te1Cyrd2n7Tvn+WFm2rFVfayV53ufRY+/M3Dtntsxvzr33nCPouq5jYGBgYGAQIsRwG2BgYGBgMLAwhMXAwMDAIKQYwmJgYGBgEFIMYTEwMDAwCCmGsBgYGBgYhBRDWAwMDAwMQoohLAYGBgYGIcUQFgMDAwODkGIIi4GBgYFBSDGExaBHOBwOvv3223CbERSXy0V9fT2apoXbFAOD4wo53AYY9E2uvvpqVq9e3fzaZrOxcOFChg8fjtVq5dJLL6WwsJCLL76Y3//+963av//++9xzzz1t9i8IAomJidx///3MnDmz1f4bb7yRa6+9lnHjxnXL/jvuuIN3332X9PR0cnNz+eyzz1i8eDE+n6/ddrfccgtXXHFFl8/3+OOP87vf/S7ovk8//RSv18sFF1zQ5X6D8Ze//IXrrruOwYMHd+r47du3I8syY8eObbXvtttu48YbbyQ9PT0kthkYgCEsBm3w4osvtni9du1azj33XPbt28epp55KRUUFr7zyCk8++SSzZs1CURQcDgexsbEAXHjhhVx44YXtnmPt2rU8/vjjvP766632XXPNNfzsZz9j7dq1mEwm7rrrLj744IOg/ei6jqIoLF26lNGjRwPw9ddfs3HjRiIiIjjvvPOIj49n/fr1XXoPfv3rX/P9999jMpkwmUy88cYbzf0fzZtvvtmmsFRUVODxeNo918MPP8yUKVOYP39+h3YVFxfjcDg6voAmvv/+e6xWa1BhKS4uxuVydbovA4POYAyFGbSioKCACy+8kIaGhuZtH330EXfffTdxcXFce+21vPbaa6xatYpZs2bhcDj4yU9+wqpVq7p0nkM37GBMnDiRyy+/nHfffReAu+66i+zs7KB/OTk5/OY3v+GHH35obq+qKhEREXg8HoqKikhOTiY7O5tTTz2VKVOmtPo7/fTT2bVrV3N7v9/P999/T05ODtnZ2VxxxRV8//33Xbq+zqDrOn/84x/Ztm0b8+bNC3n/3eEvf/lLlz9LA4MjMTwWg1YMHTqUK6+8kjPOOINly5axcuVK9u7dywMPPADA9ddfz4YNG/jqq6/IyMjg2muv5dZbb+W8885r0c+PfvQjSktL2zyPw+Fo1QagtLSU1157jVtvvbXTNlssFlRVbbX9rbfe4swzzwTgueee4/rrr+fSSy9tddxrr73Gu+++yx133AEEhCkpKQlZDvxEIiMj2bhxI48//njQ8+/bt48pU6YAgRvzxRdf3KHN1dXVLF68mAkTJvDqq68iCEKnrhXgggsuwGKxtLn/pZdeYurUqZ3u7xAvvPACmzdv5t577+1yWwODQxjCYhCUhQsXYrVaOfnkk5k6dSpvvvlmi/3PPfccl1xyCdnZ2SxbtoyJEye26mP58uXtnmP58uVBh6fq6+vZuXMnAF6vl4svvpiCggJEsaWDbTabefbZZ9u9gT755JO8+uqrAGia1jxUdzRRUVF0VEFi2rRpPPXUU0H3zZ49m7Vr17bb/kj+97//cd9993HfffexcOHCTrc7xNKlS4MObXUXTdO47777+Oabb/jwww9bvdcGBl3BEBaDoBQVFfHcc89xxRVXUFxcHHSC/eqrr2b48OH885//5NFHHyU+Pr5L56ivr2/zRn+ItWvXkpSUxEcffdRq39KlS/nwww/bFZa4uDiGDx8OwKJFi7jtttu47bbbWhwjCAI2m40lS5Z0aLPX66WiooIhQ4Z0eOzR6LrO559/zt///ndGjRrF119/TWJiYpf76Q533nknjz32WItthwR33bp1XHnllZxwwgksX768XU/IwKAzGMJi0ILc3FzuvfdeqqqquOeee5g2bVqHbb744gsWLlzIggULmDFjBn/605+CHuf1eikvLycjI6PF9hdeeAFJkrj44ov5y1/+0mKf2+1u8yYeFRWFoijt2va3v/2Ne+65hwceeIC5c+eybt26Dq+nPaqrq1mwYAHLli1rFqzOctlllxEZGclTTz3V7dVu3eWee+7hyiuvDLrvzTff5F//+hfTp0/vVZsMBi6GsBi0oK6ujuuuu46srCxOOukkdF1vd1jE6XQyf/58vvvuO9auXctJJ53EggUL+PTTT1mwYEGLtrt37+ahhx7i5Zdfbt7297//naKiIp544oljcj1z5szhH//4B7W1tVx11VXs3LkTu93e4pjt27czYcIEVFXls88+a17Gu3HjxuZ5k5qaGu68807S09N5+umnOe+881i5ciXJyclBz3vgwAHOP/98dF2npqYGXddJSEgAAgLTHosXL+YPf/hDD6+88/zrX/9i5MiRvXY+g4GPISwGLTj01Lpq1SpOPPFEnnnmmXaP13Wd6dOnI4oiJ510UvP2VatW8e9//5s33nijxRBZdnY2Pp8PTdP4wx/+gMfj4fnnn2/3HM8880zQpcYOh4NFixZ1eE2nnnoqb7/9Ng6Hg71793LWWWfx9ttvEx8fT11dHfPmzWPDhg3cdddd7Nixo1lYZsyYwTfffAMEvKoj+/vTn/7EJZdcwldffYUkSa3OmZGRwZYtWwB4+eWX8Xg8XHvttaiqyty5c3nuuec65bV8//33XHbZZc2idIhg163rOrqus3LlylbHGxj0JoawGATF6/V2avxfEATMZnOr7Q8++CBLly7ljDPO4N1330UURRoaGiguLubWW29lxowZTJgwgeuvv77Dc1x77bXcddddrbavWLGi+cbfHpIkUVlZ2Xw9//d//8c///lP7r//fu6//37+9Kc/IUkSkiR1OIF/iJ///OesWrWKO++8k/vvv79TbQ7Z8uSTT/LLX/6Sr7/+Ouh7dyTLly/nqaeeCrp6Lhg333wzO3bs4NRTT+20TcFoaGigtLSUMWPG9Kgfg+MTY+mHQVDMZjNlZWUdHufz+fB6vUH3XXDBBTz77LN89NFHXHDBBXg8Hs455xzq6upoaGjolKj0hBdffJHTTz+dF154gTPOOANFUTjppJP4xz/+wWOPPcaUKVN47LHHeOCBB/jxj3/c5f7/9a9/8f333+N0OrvUbtKkSVx++eWdun5VVYmKiup03yaTKSQpbLZu3doqSNbAoLMYwmIQlOnTp1NcXMy4ceOIj48PGlQYERHB7NmzOeecc9rsJyoqitdff53XXnuteT7i6aef5oMPPmi1SikYFouFmpqaoPtcLlfQYahDXH311XzyySfk5OSQnp6OLMusXr2anJwcnE4nVqsVv99PdnY2n376aYe2HI3NZuPbb78lIiKiy21/85vfYLFY+Otf/9rltr2B3+9vjuExMOgqxjfHICgREREsX74cn8/HrFmz2LRpU4uJeI/Hw9lnn91uAso33niDRx99lLfeeovRo0eze/duIHBDXrZsGYsXL2bdunU888wzxMTEBO1j5syZPProo0ydOrXFMJUgCIiiyL/+9a92r+Poifq+xL///W+uv/56fv3rX/Pvf/+7w2GxUFFTU8Ozzz7L1Vdf3eYx+fn5nc5FZmBwNIbHYtAuZrOZiRMnNk9EH2LZsmWcf/75QdtkZ2fzox/9iM8//5yvv/46aH4ti8XC22+/zZlnnsmsWbN48skngyaIjIiIYNmyZWzZsqVFGpctW7awadMm5syZAwQC/LoSuR6MI4eQJEmipqameZvD4WgWVr/fz5IlS9i3b19zu46WPR/J559/zi9+8QsEQeCpp55i6tSpnHzyybz77rv4/f4WxwqC0KW8YJqmtfLiZFlm/fr1vPvuu1xzzTXMnTsXi8VCTEwMERER5OXltTheVVVee+01Tj/99E6f18DgSAyPxaAFfr+fU045pUXSxMrKSr777rsWwYwlJSXExsY2B9kBJCcn89xzz3HjjTdy9913M3fuXCAw+T558mRqa2tbDRv94he/4Pzzz2fJkiXcfffdXZoIv+GGG1BVlaysLF577TWee+655n1H31xFUWy1bPpQippLL72UgwcPUl5ezlVXXQUE5ipOOumk5lVydrudl156qbkvu93ODTfcQG1tLU6nk4suuqhNOxMTE3niiScoKiriww8/ZNKkSfz5z39u3n/ttdeycOFCHnvsMZ588klWrlzZvG/WrFlcc801pKamdvh+CIKAruut4ojmz5/Pxx9/3LzC7Nlnn21+f37+85+zePFioqKimoXZ5XJx+eWXhzSy3+D4QtA7uwzGwKCbFBYW8sUXX1BUVMQvf/nLDqPWXS4XGzdu7HBlk6ZpbNq0iYKCAsaNG0dWVlbzvvr6+lbDa8G2HUJRFCRJ6rHX01bfzz//PNHR0cyfP5+kpKSQn8PAoC9hCIuBgYGBQUgx5lgMDAwMDEKKISwGBgYGBiHFEBYDAwMDg5BiCIuBgYGBQUgxhMXAwMDAIKQYwmJgYGBgEFIMYTEwMDAwCCmGsBgYGBgYhBRDWAwMDAwMQoohLAYGBgYGIcUQFgMDAwODkGIIi4GBgYFBSDGExcDAwMAgpBjCYmBgYGAQUgxhMTAwMDAIKYawGBgYGBiEFENYDAwMDAxCiiEsBgYGBgYhxRAWAwMDA4OQYgiLgYGBgUFIMYTFwMDAwCCkGMJiYGBgYBBSDGExMDAwMAgphrAYGBgYGIQUQ1gMDAwMDEKKISwGBgYGBiHFEBYDAwMDg5BiCIuBgYGBQUiRw22AQd+itraWm266iaFDh1JTU8PDDz+M1WoNeRsDA4OBi6Druh5uIwz6DldffTWXXHIJCxYsYOnSpWzcuJH7778/5G0MDAwGLoawGDTjcDgYP348BQUFiKKI2+1mxIgRHDx4EFEMPmranTYGBgYDG+OXb9BMTk4OY8aMaRYEm81GdHQ0eXl5IW1jYGAwsDGExaCZ0tJSEhISWmyLi4ujoqIipG0MDAwGNoawGDTj8/k4emRU07R2J+K708bAwGBgYwiLQTMpKSnU1NS02FZXV0dqampI2xgYGAxsDGExaGbatGns2LGj2QNxOp1omkZaWlpI2xgYGAxsDGExaCYuLo558+axcuVKAJYvX87ixYvRdZ3LLruMTZs2dbqNIAi9aruBgUHfwVhubNCCuro6brnlFjIyMqioqOCRRx5B13XGjx/PkiVLOP/88zvVxmKxhMF6AwODvoAhLAYGBgYGIcUYCjMwMDAwCClGrjCDgY+mgqu66a/miP9Xg6ceVD9oftAUUBUqrJk81DAfSRAQBQFRFJBEkAQBQRCIssokRlpIimr6i7SQHG3BbjZ+TgYGYAiLwUDAVQNV+6B6X+DfmlxoLDtCPBqAzo/4yqlzeL9gQpfNsJulZqFpITxN21JjrIxKicQiS13u28CgP2EIi0H/QFMDolG1F6r3B/4OiYm7NqSnErogQkfi8qkUVrsorHa1eYwsCoxMjmRCegxZg6KZkB7DhPRoIizGT9Fg4GBM3hv0Tdy1ULQBitYF/kq2gM/RK6euSz2JKQU39Mq5AAQBhiVEMD49mqxBMWQ1iU1chLnXbDAwCCXGY5JB+NH1gCdySESK1ge8kW56DiEwqHfPpkNelZO8Kicfby1t3j4o1saEJrE5eWQiU4fEIopGfJBB38fwWAzCQ/1B2PcF7FsBhT+Apy7cFjVTnzKbyYW/C7cZrUiIMDN3TDJnjEvm1NFJRBrDZwZ9FENYDHoHTQt4I3s/g31fQsWOcFvUJvUps5hceGO4zWgXsyQyc1g888Ymc+a4FDIS7OE2ycCgGUNYDI4dig/yvoHdy2DPcnBWhtuiTtGQMpNJhb8PtxldYmRyJGeMTWbe2GRmDI1HMobMDMKIISwGoUXTIP8byH4T9nwGvsZwW9RlGpNnMPHAH8JtRreJtZs4bXQSZ45L4azxKVhNxvJmg97FEBaD0FBbCNlvQPZbUH8g3Nb0iP4uLEcSZZVZODmdy2ZmkDUoJtzmGBwnGMJi0H38Hti1DLa8BvnfEb5VXKHFkTydrAM3h9uMkDMhPZpLTxjC+VMGEWMzhdscgwGMISwGXadkC2x5Hba9E0iJMsBwJE0lq+iWcJtxzLCaRH48MY0rTxrKpMGx4TbHYABiCItB59BU2PkB/PA4lGaH25pjijNpChOKbg23Gb3C1IxYrjxpKD+emIZJMnLSGoQGQ1gM2kfxBuZOVv8bavLCbU2v4EyczISDt4XbjF4lOcrCFbMyuXxWBklRRi0dg55hCItBcLyNsOE/sPYpcJSH25pexZU4ifEH/xRuM8KCWRK5cNogfnfGKNJjbeE2x6CfYgiLQUsclQEx2fifATl/0hncCVmMK/5zuM0IKxZZ5OezM7n+9JFGzjKDLmMIi0GA+mL4/lHY8gYo7nBbE1YMYTlMlEXmmlOGc80pw4wMzAadxhCW4x2vA75fAmuePO4F5RDuhAmMK/5LuM3oUyRGmvnN3JH8bHYmZtmY5DdoH0NYjlc0NRB/8vX94KwItzV9Ck/8OMaW3BFuM/okg2Jt3HTWaC6cOsjItGzQJoawHI/s/wq+uKNPJ4IMJ574sYwtuTPcZvRpRqdEcvP8MZw9ITXcphj0QQxhOZ6o2A1f/AX2rwi3JX0ab9wYxpT+Ldxm9AumDInltgVjOXFEQrhNMehDGMJyPOCsgpX3w6ZXQFfDbU2fxxs3mjGld4XbjH7Fjyemcs/5WSRGGjEwBmDMwg10trwO/54OG180RKWTdLfm/fHMp9vKmL/kOz7MLg63KQZ9AENYBio1+fDKQvjw+j5VnbFfoGvhtqBfUuP0ceN/s/n1axupbPSG2xyDMGIIywBD1VT8a5+Cp0+C/G/DbU4/xfBYesLnO8o5a8m3fLDF8F6OVwxhOYLa2lquvPJK7rrrLn73u9/h8Xg6bHP77bczePDg5r+ZM2f2gqXBKWwo5P8++z+eLvse/K6w2dHfEYxpxx5T5/Lz+7ezueaVjVQ0dPw7MhhYGKG0R3DzzTezaNEiFixYwNKlS7n33nu5//77220zZMgQDh482EsWBkfXdd7a/RaPbX4Mt+JmhyBzZvoExpcYy4m7hyEsoWLFrnI2FNRw57nj+en0weE2x6CXMDyWJhwOBytWrGD+/PkALFiwgJdeeglNa3+8XZbDq82ljlJ++eUveXD9g7ibIucVXeGOhDj8olHMqVsYHktIqXf7ufmdHH7x8gbKDe/luMAQliZycnIYM2YMohh4S2w2G9HR0eTltZ8qXtd1br/9dm644QbOO+889u3b1xvmAvBp3qdc+NGFrCtd12rfXscBXpg0v9dsGUgIGJP3x4Kvdldw1qPf8r+NRWE5f15eHs888wyZmZmdbtOd4XEDQ1iaKS0tJSGhZZBXXFwcFRXtpzvZsmULN998M0888QQPPvgg11577bE0EwBFU/j7+r9z26rbcPgdbR73XOMu9qSOO+b2DDgMj+WY0eBRuPXdrVz/xmZcPqVXzz18+HCuvfZauhK6d2h4/K677uL000/n3nvvPYYWDhwMYWnC5/O1+sJpmobVam233TPPPENiYiIAWVlZlJSU4PUeu6WWNZ4afvXlr3h91+sdHqtoCnckJ6GIxlRa1zCE5VjzybZSfvr0Gg7W9v4ik0OjEh3R3eFxA0NYmklJSaGmpqbFtrq6OlJT28+FVFVV1eJ1XFwcdXV1oTYPgO1V27n040vZULah0212NRbw0kRjSKwrGKvCeoddpQ0sfOIH1uZVh9uUoHR3eNzAEJZmpk2bxo4dO5q9FqfTiaZppKWltdmmoaGBIUOGoKqHI9obGxtJSkoKuX1L9y3l/5b/H2XOsi63fcaxl9zk0SG3aeBiCEtvUeP08fP/rOO1tYXhNqUV3R0eNzCEpZm4uDjmzZvHypUrAVi+fDmLFy9G13Uuu+wyNm3a1KpNREQEJ510EpIkAZCbm0tWVlanXe3O4Nf83LvmXu5cfSc+zdetPnyajzvS0lEFKWR2DWiMyPtexa/q3PHBdv68dBt+te+8990dHjcw4lha8MQTT3DLLbfwww8/UFFRwSOPPILP52PdunUcPHiQ6dOntzhekiQeeughHn/8cWRZJjc3l8cffzxk9lS6Krn525vZUrGlx31ta8jj1YnzuWrr8hBYNtAxPJZw8Oa6A+wvd/D0z6aR0AeSWXZ3eNzAyG7cZ8muyOYP3/yBSndlyPq0SBbeqdcYVpkbsj4HIpo9keE1oXtAMOgag2JtPLd4OhPSY45J/0OHDqWgoKDD42pra5kwYQLFxcUIgoDT6WTKlCns3bsXQTCKnLWHMRTWB3lv73tc/fnVIRUVAK/q5c70DDTB+NjbxXjUCivFdW4uenoNH28tOSb9+/3+5iEuTdPaHOpua3jcEJWOMe4wfYxncp7hrjV34df8x6T/7IZc3sgyVom1j6Es4cbtV7nhzS088vmeLsWdtEdubi4PPfQQJSUl3HzzzWzatKnFUHcwnnjiCd566y3uvfdevv32W2699daQ2DLQMYbC+gi6rvPwhoc7FZ/SU2ySlXfr/GRU5R/zc/VHNGscw+ueDLcZBk0smJDK45dNxSwbz8H9BeOT6gMomsJfvv9Lr4gKgFv1cOfg4egYLn1wjGetvsRnO8r45asb8fiNQnX9BUNYwoxH8fD7lb9nWd6yXj3vpvp9vGUETgZFMJYb9zm+3VvJVS9twOnt3TQwBt3DEJYw4vK7uP6r6/n2YHgKcj3mKeBgfEZYzt23MTyWvsiavGoWv7ieBs+xmX80CB2GsIQJp9/JdSuuY33Z+rDZ4Fbc3JUx2hgSOxpj2rHPsqmwliueX0edq3vBwga9gyEsYaDR18ivv/w1mys2h9sU1tXv5Z0JZ4bbjL6FISx9mm3F9Sx6bi21TkNc+iqGsPQy9d56fvXFr8ipzAm3Kc086iuiNG5IuM3oQxjC0tfZXdbIz/6zjnqXMSzWFzGEpRdp9DXyyy9+yfbq7eE2pQVOxcVdQ8eG24y+gzF53y/YUdLA4hfX0WjMufQ5DGHpJfyqn9+v/D27anaF25SgrK7bw9LxxpAYYAyF9SNyDtZzpbFarM9hCEsvoOs6f/nhL2GdqO8M/1BKKI9JD7cZ4cfwWPoVmwpruerlDbh9RpxLX8EQll5gyeYlLM/v+1mFG/0O7hk+Mdxm9AEMj6W/sT6/hl++urFPpd0/njGE5Rjz1u63eGn7S+E2o9N8V7eLZePmhduM8GIMhfVLvt9fxR0f9K35y+MVQ1iOIV8d+IqH1j8UbjO6zENaOVVRKeE2I3wYQ2H9lv9uKOKlH4wceOHGEJZjRE5lDn/67k9o/fAm1eBr5N5RU8NtRtgQjKGwfs19n+xi1b7Qlpww6BqGsBwDChsK+e1Xv8WjesJtSrf5unYny8fODbcZYcQQl/6Kqunc8OYW8quc4TbluMUQlhBT7a7m2i+vpdZbG25TesyDejXVkUnhNiMsSIIhLP2ZerefX7yywcgrFiYMYQkhbsXNDV/dwEFH8KJB/Y1aXz0PjJ4RbjPCgmhUCez35FU6+e2bW1A14yGhtzGEJYTcvur2PhdV31O+qN3Bl6NPDbcZvY5E/5sbM2jNt3srefDTvhmUPJAxhCVEvLz9Zb468FW4zTgm3CfWU2ePD7cZvYpkeCwDhhe+z+d/G4vCbcZxhSEsISC7Ipt/bf5XuM04ZtR4a3lw7Oxwm9EtCuu653kIguGxDCT+unQ7mwprwm3GcYNR876H1HpquXjZxZS7ysNtyjHncTmD0/d9H9I+V+QprMxXSLQLlDl0bptjId7WvreQXabydb6CSRTYVqFy3QwzU9Mklu/zs75YI9oCN51oYU2RgiDA7MFyl+2arL5Kvb/r7Qz6LomRFj664WTSY23hNmXAY3gsPUDTNW5fdftxISoA98oO6m2xIeuv2qXx8A9e7j/Dyk0nWrhxtpkbP+t4ifafv/LyhxMt/HaWmfvmWbjuk0Cbpzf6+dtcC+tLVDyKzrpitVuiAsYPYyBS5fByzSsbjZxivYDx++kBOV++xdqS1eE2o9eo9NTw8LiTQ9bfD0Uqk1Ok5tfpUSK7q1Qcvrad6Fq3zneFCm5/4JjkCJEqV2DYytW0TRYFShp1Is3dnycxlhsPTHaWNvDQcmMy/1hjCEs3cWdnY/3DQ7z+6QhG+RPCbU6v8VHtNr4bcVLI+jv64dHhg/01bc9vxNkEPrncjrXJEalx6yRFBL7GyRECPlXHr+qsO6hy3ujuD2WJxtz9gOXVtYWsya0OtxkDGkNYuoHqcFD8x1tAUZBydvPAf7z8rG58uM3qNe6xeHBYo3vcz6xBEl/mKdS6A97By9k+Gr16s+fRFqcNlRGaVm0tWePl7rkWAO6bZ+XFLX6ummKmxq3zQ5HKMxt9aN2YRpSMyPsBi67Dre/lGDVcjiGGsHSDsnvuwX/wcBCkXlvHwme28a/tU7HoUjstBwbl7ioeGX9Kj/tJiRR5+hwrt63w8KcVHsYnSURZBJIjOve1/DJXYUS8yPwRAc9keJzItTPMjE0UsciwvlglI0bgvZ1dv4GIxlDYgKaoxs2DxpDYMcMQli5Sv2wZDR8ta71D10lbtoFXPxzMJN/Azwz8Xu02Vg+b1eN+Thsq89x5Nh4608rMQRJVLp1BUR2PQ20qUTnYoHHlFHOrfV/kKsRaBYbFioyIE1lzsOuTtcZI2MDnjXUH+GF/VbjNGJAYwtIF/OXllN19T7vHCLtyuePZOq6tGvgFs+62qbgskT3qo9J5eD4lv1Zj9mAJm6n923phnca3hQpXTTU3tzs0ma/rOjqgaGCWQBKFDofWgmFM3g98dB1ufXcrDmNILOQYwtIFyu+7H83h6PA43eFk3vNbeGbTFKI0Sy9YFh5K3BU8OuG0HvUx5yUXG4oDHsXzm33cOMuMputc9p6LTSWtPQ2/qvPcJh+/n33YU3l2kw9L0zz9d4Uqp2VKjEkQqXbrVLs0RsR1/WsuGS7LcUFxnZv7P9kZbjMGHIawdJLGb76h8csvu9Qm/ouN/OedBE7yDDlGVoWf/9VuZ/3QE7rd/pfTTLyU7eOfq72MjBc5c7iMT4V1BwNDXUfzRa7CM5v8ZCxxMPjRRlIeaeS9XUpz0siiBo0xiRJT0yQavTr/26Hw88mmLtslGpP3xw1vrS/iu71G/ZZQYkTedwLN7Sbv3PPwFxd3q71gtbJ+URb/SMsOrWF9hMH2VN7btwO7b+DUv5gv/Ye9TiNC+3ghLcbK5zedSrS16w8hBq3pVWGpra3lpptuYujQodTU1PDwww9jtVrbbbN69Wo+++wzkpKSKC4u5qqrrmLMmDEArFy5kh07duD3+9mzZw8PPfQQsbGxIbe74p//pPr5F3rcj+O0qdw6O48qceDcgA9xRewk/rTl43CbETIWyC+w22EPtxkGvcjF0wfzj4snh9uMAUGvCsvVV1/NJZdcwoIFC1i6dCkbN27k/vvvb/N4TdM499xz+fTTTwFwu91cdNFFfPLJJwBkZWWxbds2BEHgo48+YsWKFTz++OMhtdmzdy/5F/4UlNBM8AmD03n2pxGssA+sutwCAi+rCUw7sDncpoSEH8vPs9MREW4zDHqZl648gdPHJofbjH5Pr82xOBwOVqxYwfz58wFYsGABL730EprWdpR1dXU1R+qezWbD4zmcS+rHP/4xStMNf8yYMRQUFITUZl3XKbvr7pCJCoB+sIRfPZnP3wqnMZAWHuno3BltxmMaGMNHxuT98cnt72+j3m1UnewpvSYsOTk5jBkzBlEMnNJmsxEdHU1eXl6bbZKSkqiuruaFFwLDUGVlZYwbN655/8MPP4zJFBgTfeedd7jqqqtCanPdu+/i3nwMnsAVhQlvrufVleMYrMSEvv8wUegs4YmsM8JtRkgwAiSPT8oaPPz7q33hNqPf02vCUlpaSkJCy5xacXFxVFRUtNvugw8+4MEHH+TUU0/lwQcf5JFHHmmxPz8/nyuuuILS0lLOP//8kNmr1NZS+cg/Q9ZfMCzrtrHkFYELGkcf0/P0Jq/VbydnSP8fpzY8luOXV9cWUlznDrcZ/ZpeExafz8fR0zmapnU4ef/kk0/y9ttvc/311/P222+zfPnyFvuHDRvGG2+8waxZs7j11ltDZm/F3x9Gra8PWX9toVdUcdlTu3l47zSkARDvrekad8ZG4JP6d/yOYCyWPG7xKRqPfrE33Gb0a3pNWFJSUqipaVnBra6ujtTU1DbbbNu2jcbGRmbMmMGll17K8uXLufHGG1HV1oFzixcv5uWXX8bt7vmThmvTJuo/+KDH/XQaTWPoe+sDmZKV/p8pOc9xkKcmnRluM3qEJBrCcjyzdMtB9pQ1htuMfkuvCcu0adPYsWNHs9fidDrRNI20tLQ22+zevZvx4w9nDZ46dSppaWnU1tbicrk499xzW03++3y+Htta8Y9HOj7oGCDl7OaBF7z8fABkSn65fic7BvXftDb933c06AmaDv/4fHe4zei39JqwxMXFMW/ePFauXAnA8uXLWbx4Mbquc9lll7Fp06ZWbWbOnEl2dnbza6/Xi91uJzExEafTyYYNG5oXA+zZs4esrCxiYno2Gd749de4jzhnb6PX1nHeAMiUrOoqf42Pxi+1ThLZHzByhRms2FXBhoKajg80aEWvxrHU1dVxyy23kJGRQUVFBY888gi6rjN+/HiWLFkSdPL9yy+/ZMuWLcTExFBSUsKiRYuaV4b973//Iz8/n6ioKPbu3cstt9zCoEGDum2frmnkn/8TvPv6xqoQfdwI7v+xh63m/lX62Fflw5wYEJRfx0zkhuxPwmxR1/mZ/Wm+rxk4K/YMusf0zDjeuy50he2OF4yULkdQ/+GHlNz2p3Cb0QIhMoKvF43h6aStIeuz7J0y6lbXNb82xZoY8bcR7bbRdZ2ar2pAB0+RB9sIG/GnxdO4tRF3nhvRJpJ4diKu/S4A7CMDUeuyIPOW28rY0v6V6O//7E/ybU1cuM3odyj1FcgxAyvA8LmfT2f+hLbngg1a0/3arQMM3e+n8t9PhNuMVugOJ6e/sJkp86fzx6m7aRS9Pe7TFG9i7JKxXWpT/Xk1UZOjsKRZ8BR5cO4JpKWp+bqGzN9nUvR0EZpPw5XrIvHsxOZ2iq5wR1ICb5abMGn9J/CsP8+x+Mrz8BTmgCTjrywgauqPMaeMoPbbl3FuX9l8nBQVT9riJR32568rw5O/mfq17zD4upeat7tzN+It3YtosRN9wk/wFu8ChAEnLP/4fA9njEtBMupVdxoju3ETde8vbVEVsq8R98WmkGVKFroYpKGrOo7tDixpgSXE1iFWEs4MrF7TfE2LJ0RQ6hREa+uv1O7GQv4z8ayeGd3L9Od7SO13rxA98wKip59H7Ck/p/rzpwCQo5IYfP0rzX+dERUAU2wqUVN/zNEJnxuzPyV2zuV4S/eiKz68JXuxDOraA0t/YF+Fg/c29d17Q1/EEBYC3kr1s8+G24yOyTvATU+XckvJlJ71oweGw0peK6FwSSHesva9IM8BD6JVpPLjSopfLCb/4XwcuwJ1aeRoGU3RQAVXnovoKdFB+3jOsYd9KWN6Zncv0l/T5qseB96iHWj+wGcqRcSiuRsCO8UeLgYRWqqt3nQOQZRQHDUI5vZj0vozS1bsxePveiXS4xVDWIC6Dz7AX1ISbjM6he7xcMIrG3nph4kkat1LkugudJO4IJH0n6eTcnEKJa+0f+2+Kh+uXBcRYyIYdPUghlw3hLK3ytB1nZQLU6j7ro7YU2JRHSrOfU5qvq5B11remP2anztSU1GF/rHSrb96LJI1kuSL/oYgBxZPqO5GRPshsdep/fZlar58mop378Zf070yEIcQ7bHoqh9dVfCV7ME+cmYPre+7lNZ7eHVNQbjN6Dcc98KiKwrVzz4XbjO6TMR3W3j6dTvzncO73HbQlYOQowLTa9bBVpQ6Bc3fdjJQzadhHWLFPqppQj5KxhRvwlfuw5xsJn5ePJY0C4JJwJ3nxpRgomFjQ6t+djTk89Kks7tsbziQhLbfj76ONWMiQpN30bjhA2LnXAGArzyX6BMuIP6s64g97f+o+eLJHp0n9tSf49j6JZETz0TzNOI9uIvGLZ+i6/33vWuPp77JpcHTf+YJw8lxLyz1H37Yp+dW2kMvLuWap/K6nClZaWyZrVmMENFcbd8MJKvUfKNq3hYpoboODw04tjuQ7BLmJDPmZDOuXFfQvp527CUveVTnjQ0T/dRhaYE7fwtyXBq2YdMASDj7BiR7YAm1OWkoSmMNutL9G+WhuRdTwhCQTHhL9yJHJ+Haszok9vc16lx+/rv+QLjN6Bcc98JS/Z8Xw21Cz+hipmTVrbLnD3taDFVpHg0pqu0hKstgS6t5GM2tYYoLZJY+tGJdV3UEWQARNG9wofJpPu5IG9Tnh8T6+w/DW7YftbGKyImHU+uorpa57yRrJJrX0eNzufM3I1oikGNTkGPTmlaHDUxeWV2IqvXP+bfepL//fnqEc80afO2k7e9PdDZTsmgRsY+0IzRNIngrvFgHBSZdi54uwl3QOteaJcWCFCk1x6joqo5gEpqFxbUnMP9iSbOgOlRUh4o5ue2I+60Nebw2cX63rrO3EPvxUJhSX4H3wDYiJwVW4vnrytC8ToqfvgpdO+xlaj43or1nQaDNYXCaiiDKIIroSs+XxPdViuvcfLmzLNxm9HmOa2GpffOtcJsQUo7MlCzrwT9aQRRIuTiF6i+rqf6qmpqvaki7Ig1d0XHlufDXBB8aGXLtEKq/qqb6y2oqP6kk5acpzfv8NX4saRZsmTZUj0r9+npiT4pt19YnXPspSGo/KDOc9NfJe11VaMz5jKgTDmexcGR/hmCyYhk0FqFpZZi/thRTUiaCIKLrGpUfPYy3bH/7nWtKqwzl3qLtWIdkYUoYjOpuRHM3Ise2nf9vIPDi9wXhNqHPc9xG3vvLy9l/xpkhrQ7Zl1Anj+XO+bXsk6vDbUqbTI0Zycs53yD2wcneP8Qs4f3ylI4P7GO4cjdQ/fGjzavCdE1FtNgY9Kvn8ZbuxVu8G0GU8NeVEjP7YiR7DLrio+SF64g745fYR81u1ae/thTXnu+p+/YVomacT8SE07GkjgTAsWMlkRNOB6Bu1RtofjcxM3+KFDmwsxZ8/Ns5ZA0yUv60xXErLJWP/5uqp54KtxnHFCEulo8WZfBabN9Np3Jb5Hh+tu2zcJvRij/GLuHdsv4nLAa9w4XTBvHoJVPCbUaf5bgcCtMVhbp33gm3GcecQ5mSH9/WdzMlP+7OoyhhaLjNaIXQTwMkDXqHj3NKqXIM3LmknnJcCkvjihUolZXhNqN30HVSP97Aqx8MZoqv7yXSc6se7hwyAr2PLfA10uYbtIdP1Yw0L+1wXArLQJu07wzC7lz+8mwtv6mcFG5TWrGxfh9vZ/WtXGLH5Q/DoEv8d0NRuE3osxx3vx9vbi6u9evDbUZY0B1O5r6wmWc3TSZK61s16Zd4D1AcnxFuM5oR6XsLCgz6FvlVTtbk9t3FMeHkuBOW49FbOZpQZkoOFS7FxV2ZfSdJpSj0raE5g77JW0YkflCOK2HRXC7qP/ww3Gb0DUKVKTmErK3bw7sT+saQmDF5b9AZPttRRq3TF24z+hzHlbA0fPY5mqPnKSwGCqHIlBxq/uk/SFns4HCbYQyFGXQKn6Lx3mZjEv9oji9hWb483Cb0SXqSKTnUOPxO7ho2Ptxm9NvIe4Pe5/3NPSs/MBA5boRFravDuXZtuM3osxzKlHxXwdQuZUo+FvxQt5sPxp/Z8YHHkP5a6Mug99lZ2kBxXesce8czx42wNHz5JfiNWgrtoiiMf2sDr349tlOZko8lDyslVMSEL+eU4bEYdIUVO8vDbUKf4rgRlsblfS9tSF/Fsn47S14WuLCDTMnHkka/g3tHhC/mRgy322bQr1ixyxCWIzkuhMVX78CxfeDWiAgVxUd4dHplFYs6yJR8rPmmdhcfj50XlnMLx2cKPYNusi6vhkajumQzx4Ww5O5oYNWsB9h3+RPUL/gVWkxiuE3qFo9WVnB67v7mv0sLCzpsU+Tz8d+6Ws7IbZkS/TuHgyerqnilpgaAbLebqqMzPWsaQ99bz2vLhzNKSQjVZXSJh/QKqiKTe/28xlCYQVfwqRrf7j1O0kR1guNCWPJzqlD8GkUlAps8k/luxj3suewpas+9ATVxULjN6zRpsomVI0Y2/72dObTDNkPMZhbFxrWain67ro7rExPZ5nHj1TS2ut1MttmC9iHl7OaBF7z8vK73V2vV+xq4f/T0Xj9vfy70ZRAevtpVEW4T+gxyuA041ig+laJdNS22aapOcSkUMw5h0jhSkkVS3fuI3vABcknfrSgp9SAa/OgnCHdTDRRJEKhQFOxi+88YgUzJ9ZxwzgxuzdqGR+i9OjYranfw2ZjTWLDn214753HxxGUQUlbuqUDVdCTD3R34wlK0qwbF1/bTp65BWZlGGSNgzM0knSKR5i8gZvMyTAU7etHSjtHRebSyApemUeL3c2tyCkPNbZcAbo8EScan6yi6zlaPh7mRkZ0wIJAp+ZX9I3jwHC/Z5t4r0fqgUMvMiATinb2Tm8lYbmzQVepcfjYU1DB7eHiGjfsSA/7BLD+nqvMH61BZrrK1Zgirhv6GnIufpeKiO/CNnnHsDOwCuzxeroqL568pqdyUlMzd5d2/sf8uMZH36+u4ICaGelVli9vNf+tq0ToxaR2OTMk13joeHDOz185nPHMadAdj2XGAAS8sBdu7/4RbXamwvSqV79OvYvNPn6PsknvwTDolhNZ1jbtSU4mTA07mKIuFSkXBp3VvLuDQ3MswswWzILDN4yZNNvGFo7FT7Q9lSn5u42RidGu3bOgqn9Xu4KtRvfP+C8ZyY4NuYCw7DjCghaW2zIm7ITQJ4uqq/eysSGB1/CI2/OQ5ii99AM/0+ei9mAW39qhVW9GiREM3heUQq51OoiSRQSYTGWYTOe6uRRDHfrmJF96O77VMyfdKDdTbj309dWMozKA7FFS72F/RuYezgcyAFpbS/fXHpN/GOj97ymNYHXU+6897hqLLHsZ14kJ06dhNWTlUlXl5uahHDFU5NY14qfslh3VdRwdUHUyCgIiAW+v6DVXPb8qUXDyl27Z0lmpvLQ+NPfGYn8cQFoPu8uVOY3XYABeWumN+DmeDwr7SCNZazmbNgicpvPyfOE69BN0c2uEhmygyxWZrXhl2wOdjlCUwcf/HkmJ2eDzttlcICMmRbHS7OcFuY6jZTJ2qUqeqDDGbumWf7vFwwqsbeemHrGOeKfnj2u18O/LkY3oOI/LeoLsYw2EDXFhKekFYjsTjVMgtsbJePI0f5j1G3uX/ovGMxWi2Tqy46gBJEPhDYhKv1dbwVm0tb9XV8ufkFPxNq7rK2siDdsDn4/nqaioUhYcrK1oIUKnfzzCzhfFWK05N47PGBhZG9yxHWMR32b2SKfkek4sG27HLZzagfxgGx5QtB2pxentvOX5fRNCPfowdIDjrvLz8px/CbQYAslkkPVEhqXorUWveR2w4DsqZyjI7L57G3Zmb0Y/RNNRP4iZy7+ZPjknf/0m7k3vzxx6Tvg0GPu9ceyInDI0PtxlhY8A+mPW2t9Ieik/jQInIJu8UVs28lz2XP0ntOdejJoQve+8xR1EY/9b6Y5op+YPabXw/4tjMt4gD83nLoJfYdvDYzO/2FwZsgOSxmrjvKaqiU1wCxYxHnDyBlBSBZOdeYjd8gFSaH27zQo5l/XaW5Cfy1qWjeT9qb8j7v9viY6k1mkhPQ0j7NUreG/SE7cV98/7TWxgeSxjRNJ3SUo2chpF8N/aPbL/0GaouuA0lY2ANwRzLTMll7kr+OT70sS1GaWKDnrDNEJaBh9etUFPcv2rb6zpUlKtsrc3gu+G/bYr6/yu+UdPCbVpoaM6UPCzkmZLfrd3G2mGhjco30j0Z9ITcSgcu3/E7gR+yobDa2lpuuukmhg4dSk1NDQ8//DBWa/tLblevXs1nn31GUlISxcXFXHXVVYwZM6bHtpTl1tPfh8irKxWqSYNBvyB20q9Jl8qI2/kllu3fh9u0HiHl7OGBwhiWLZrAq3Ghy8V2l13nfUskdm9oHigEI47FoAdoOuwoaThuJ/BDtirs6quv5pJLLmHBggUsXbqUjRs3cv/997d5vKZpnHvuuXz66acAuN1uLrroIj75pOerfNZ+kMumzwp73E9fJDrORLqlkvi9K7Fs+apfF6QqO/eEkGZKXhQ3ib9s/jgkfb2Zfjt/zpsYkr4Mjk/uPHc8V88ZFm4zwkJIhsIcDgcrVqxg/vz5ACxYsICXXnoJrZ10I9XV1S0C9mw2G54Ogvw6S2XRwE2p0FDrZ3dZLKujL2D9ec9w8LK/4551LrrY/Qj8cJH68QZe+WAQU3ypIenv7dptbMgMTcLQATlGbNCrHM8T+CH5/eTk5DBmzBjEppoeNpuN6Oho8vLarm2SlJREdXU1L7zwAgBlZWWMGzcuFOZQU+IMST99HWeDwt7SSNbYfsTaHz1F4eX/xHnKxWhmS7hN6zShzJSso/O3SBG32d7jvozJe4OecjxP4IdEWEpLS0lIaDkhGxcXR0VF+zlzPvjgAx588EFOPfVUHnzwQR555JEe2+JzKzhqvT3up7/hbor6XyfNZfUZ/yL/8sdonPczNOuxTa8SCkKZKbnIVcbjE+b12CbDYzHoKcfzBH5Ifj8+n69VHipN0zqcvH/yySd5++23uf7663n77bdZvnx5j22pKT0+vJX28LlV8ktMbNBO5PtTHyH38sdpmP8LtKi+PZF4KFPyyT3MlPxm/Xayh0ztUR9G2nyDnqLpsLMktPFV/YWQCEtKSgo1NS3L/9bV1ZGa2vbY+bZt22hsbGTGjBlceumlLF++nBtvvBFVVXtkiyEsLVF8GoUlEht901g16z72XvYkdT/+DVp8aOY1Qo2ef4DfP13KrT3IlKzpGnfEWPHK3fd+jFVhBqHgeB0OC4mwTJs2jR07djR7LU6nE03TSEtrO2XJ7t27GT9+fPPrqVOnkpaWRm1tbY9sOV7mV7qDqugcLIXNrgl8N+VOdi16ipqFv0dJyQy3aS3QPR5mNGVKTla7l8CzwFnMkxPP6LYNRnZjg1BwvKZ2CYmwxMXFMW/ePFauXAnA8uXLWbx4Mbquc9lll7Fp06ZWbWbOnEl2dnbza6/Xi91uJzExsUe21FW4etT+eEHTdErLdLIbRrFq/K3suPQZqi+4FX9Gz+OIQkXEd9k8+bq125mSX63fybbB3VsU0J+XcRv0HXaVDdwVqu0RsjiWuro6brnlFjIyMqioqOCRRx5B13XGjx/PkiVLOP/881u1+fLLL9myZQsxMTGUlJSwaNGiHq8Me+Nva6krN8SlJyQmy6SpB4jN/gRTbna4zQFZZtdF07hraNczJY+IHMz/dm7CrHZtQceywTfz2/3Tu3YyA4OjiLOb2HLn/HCb0esMqLT5uqbzzO++QVMGzCWFnbgEmTSxlLgdX2DZuTqstnhnZnH7aaUclLs2vPDLmIn8LrtrgbcfD/4DN+wPTUyMwfHN3vt+hFk+vtYZDqirbazxGKLSDjWNXa9sV1utsLMyiR+Sr2DjBc9Rcul9eKZ2f+6iJ1jWb2fJywIXNo7uUruXGnaxM31Cl9oYcSwGoaLScfyFPwyotPn1le5wm9AjPlz3Ahv2fdn8OsaewC0XPtVum6qGEnYd3MQXW97k3iveat6+48A6Civ2YDXbmTfpIvLKdiAIAvFRKd22r6HWTwNxEHMhEQsvYVBELfG532Hb+DmC1rPVfJ0lkCm5htkXzODPo7JRhI4FQNEV7kiI479lJkxa8EqbR2PkoDQIFeUNHgbF2sJtRq8yoISloap/C0tcZBL3/eztLrVJjE7nlPHpfLHlzRbbV+1cxrUL7uOlFffhV3wUVuzm9Ek/DZmtzgaFvQ1RYD8H24/PZ1BUIwlFa7CtXYao+EJ2nqAcypQ8eQx3zq9jn9xxRc69jgM8P2k+v+nkkJix3NggVFQ0HH8ey4AaCnM7Ovc02lcRhe7n+xKOesb2K4EvsyhK1LuqsJh6FtHeHm6Hwv5SG+vkeaw563HyL1+C4/QrjnnUv5Szhwee97C4tnPDXM837mJP6viODwREQ1gMQkRlY2hyIPYnBpTH4nH2b2EBnQ/XvYDX76KmsZwLT7qO5JjB3eop0haLovpRNYWCit1kZR6bEr5H43Wr5LvN5HMSptPmkJ7gJ6lyC5FrliI66kJ+Pr2unnOfyWFGJzIlK5rCHckJvFkhI2vtp9owPBaDUFFueCz9G28/91iKqvZzxqSLuWTO71g46xr++91j3e7rvBOuYs2ez5g9ZgFOTwN5ZTtYtXMZmt57k9J+b1PUv38Gq058kL2XP0H9j65FjUsO+blSP97AK0vTO8yUvKuxkP9M7Hj5p1HoyyBUVByHHsuAEhaPq38nfLvs1JuItMUAkB4/jHpXNX61e/MVgbmX80iNzcAkmSis3E18ZDLZeatCaXKnUf0aB0sENrknsmraXey+7Clqz7sRNTkjZOcQ9uTxl2c6zpT8rGMP+1LaDwQ1PBaDUFHRaHgs/RpPP/dYHO6W8Rl2SyTuHlZE3HVwIzZLJAlRqSRGp5NfHrqqjd1FU3VKSnW2NI7muwm3sWPRM1T95BaUIT2P+tedHWdK9mt+7khNRW1nTkswlhsbhAhjKKyf05/nWNw+J3e8sQjtiGW7Hr+bSFtst/vUdR1d11E1FUk0IQoiPqVvfcl1HcrLVLbWDeW7kb9j6yXPUvnTP+Mb0bP6LB1lSt7RkM/L7QyJGSNhBqHieJy8N4Slj2CRrQxLmYDYVAmysr6E9LihALy04j4OVO5tt72qKa1KF+wv3cqo9MmkxA7B6W3A6WkgKTr9mNgfEnSoqlDYVj2I74f8muyLnqX84r/hGze7e911kCn5Kec+8pJHBt1nrAozCBXVTh+Kenx5wANGWHRdx9uP51hEUeL8Wdfwzbb3+W7Hh6za+SEXnXwDqhpY1VXnrAzarrK+hC+2vEW9q5r31zzdQoBqHRWkxA5hSOIovD4Xm/O+Yebos3rrknpMTZXCjspkvk/5OZsufI7SS+/DM+X0LvXRXqZkn+bjzrQhaELrn4Exx2IQKnT9+Iu+HzC5wjxOP/+5OTwT0wa9S2SMzCB7IOrf2oWofyE9lRcujuZze8uS2X+MmsD/bW1ZZO77Ib/mZ/tOC5nNBsc3H15/MpOHxIbbjF5jwHgs/XkYzKBrOOoV9pRGscZ+Dut+/BQHLv8HrpMvQJPN7bbTS8r4xZN53F0wjSPLrTzh2k9hYsvU/MZQmEEocXr772hKdxgwwuJ1Hl8fnEEAl0Nhf4mdtaYzWTP/cQouX4Jj7mXoljZyMykK495az2tfjyVDjQXAo3q5c/BQ9COm7I3J+75H4d/PDbcJ3UbRjq8HlQETee87zp4IDFrjdankuczkMQfT3FNJT/CRXLGFiLVLER0tl3Kb12/nn/mJ/PfSMbwXtYfN9ft5c+J8rtj2OWAsN+4OwW78mbd93K02R24/+nV/RA2BsAhC68edtWvXMnt268UtH374IQsXLmyzzaxZs4Lua6/dww8/zC233NIpWweMx6IfZ08EBu0TiPqX2aCcwPcnPsS+y5+gbsGvW0T965VVXPrULh7eOw1ZF/mXp4CihECZZsNj6Tv0d1EB8B+jVWHvvPNO0O2vvfYamhb8nOnp6bz9dvBktw899BA+X/Cg7G+++abTdg0cYTF0xaANFL9GUYnAZs8kVk27iz2XPUXtub9FTRp8OFPy8mEM9ti5a8godARjVVgXaevmX/jOYyFt01/pqccyY0bwonMvv/xy0O0TJ07k0UcfDbrvySef5Oc//3nQfQkJCbz44ovdsvFIBsxQmOGxGHQGTdUpLoVixiJMvJ2UZIkU9z5iNn7IA897WLbIxDtZZzG83hgKCwl5K4Dfd6PNwKKncyybNm0Kur26OnjJiJdeeomSkpKg+8rKyvD7gy922rp1K3v27Am6rytl4weMsBgPmAZdRdegrEyljOEw+iaSkiVmVRTidu+hJH3AOPMDisJ3HiPz4t+H24wuE4o5lq5QUFDQ5r4vv/yyzX0HDhxAFIN/99vaHowBIyydCcfR0RHQAxNTgo4gcPhfAuPqgqCDAILQdLxwaJuAoOsIgo7etJ/m/YHzN/dHoH8Ems6nB4RPBI44Dpr60Q8fyxH9Hjp/i/0cOschLW1ay9S87Yi2za8D5xfQW+0PoDVd/aGndP3wv4fO33zckfuDbNP1wLXph6xrskPXm445om/98P91CNzpm/vSD3+mR7XVj2jb3K/esk3L7aAf6lvXm852VB+6jsup0xCbSULiiRRby3l8hp8qsxdnrUSkuxGnz4euifgRkHUdnyZgUiVEWcOkC3hxIZqi8Xt9aF4QRAXZJKPoGpIgISChqT4kQaDR7SQpKRZfow+P6sfvB3QRs9WKrvnQVfD5PMiiiCDrCG4/ftmM7nOhWCwIgoroFZHMEorPDQgIoo5Js4Ag4hc8qKKZRsWPhESU5kfTRTRBQUdB1mUUTQNdQxVE0H3oAaNBl5FEAYfPjSCKWEUFBQFVkZAFAb/uRwNkXQFBRlF9PEbbLPKsQkcEBEQBNF1EEHT+3k6bNslbwWL9NBBAFCyB77LmbapHJKI1fesFQUISAF1FREPHhCJoiAjomg9BsqLpvoAtkoCkB76psiShqxqa7kcXpaZ7QmDiXNeApt8/ugCa2mSHEHgfRRlBCGTG1vWmew2goTN3TN8JTC4rK2t3f1tzM6+88goPP/xwp84xYIRFUwtQnE8231ACQ2OBf/UWNzEDg5YMGTSB8ZknE+dLJj+mlvqRa2loGER67kHGRw7BnVbH3kgZm6qCS8TkVLB7G7CqHgRNox4bbr8Jsy6hi9V4VQ1N1BB0HVXRkXQVVdWRdB8qMoqiYkKjrqQKkyiAALImoOsaSqOGjgaajiSArgioPgEJAUkBJD+CX0TQRFQBNLeCKImgBm6aiqYi6DKCKCD7FeIFHV3QEXUTiDoCGqImIAk6JllG0DQEUUQXdARBRNAFNF1BEHQirCYkBHTM6JqGKuiAhqjJgIQmmNA1FUFov+xujFVA1/2IiCBKgIbehaffo4mQXIi6AHgQERBkCU1VUBHRRTADqtr0fCNJIICue7AKVtSARILqRZR0VNGCoIuIooKKhqQefujUdNAEMSA6mh+QA6Ki6SA2PQypGrKuo4sCmi7S/CjVJC46IIoQa28/xqo3aUs4OqKioqLTxw4YYRFFEcV3fKVNMOg+QzMmM3bwScR6EtBr/fjqNL5N34c1YR97Y0ZgOijjSVrDqMJE9BoHcyOz2BtZCvG70Icq7JWHUuKajrdeJstbSJq3CLvSSJQCglXDLdnwCFF4sCL5RbxmE4KgoAG6qiFqoIkSmq6AFljcLAha4CnXr6JJoKsiiCAqOopJBEUDk4iqgqRq6BKgHfY8FU1A0kEVmjxrVUOUJVRdR1BUdNkMqoIkCGiCGPDiVA1JFFAAXVdBFNE1AUHQ0QQhIBy6hICGIAhoh5xYUQBUBK3ZfW+ThMGJSIKOrmmBekAaPRKWuLR4BEAVhSbPgIAnoWmAjigIgedI6bBfLjatytIxIwgioig1ee4gHfIrJBGtybMS0EHXEDQQxKYRiybBUJs8Y5MooaGj6RIgImoBz18UVHRBQNE1TICmwLqvVjLrjK6lI+rPDBxhkbpf1tfg+GDksBmMTp9FtCsOvc4PpaDjp3yIl5WubKJ1F+kJH/KJ71VGVO/n/PQGpMYvqI9cxKaSz5mccjKW0nnsSq7C7NzDWaP3E5mwjwI5g/Wm08l3p+OuNRPR6GGkp5BxSh5jpTwEqQ7J5UFR4/HIqbgskTTKNtA1LJoNr0VCE/2gBTwHxaxiUzV0swlFBt0PFjQ0swAaCGYdTdfRFB9mJPwmE6BiU3VUXUMXZWRdQyNwsxV0E9gCrwXNBGJg4EjTdARNA0QEs4rmB1HX0QURTdBAF5uGc5oGkXUC3pWgo+qBm7Sq65g7WJv969/+EYA3Xng8IE7C4YHc7mCzWxEELSCQugaajApIIgGPCw2TCD4hIMaCoAWuiyYlEZsHQxF0oWn4WUAXhMAyWV1A1HVUBHRJRUYIeEBNnp2sg1lUUQg8/cu6hmACTZZA19E0AVXTsQsgaCKCDGbrsSsN3hcZMMIiSQPmUgxChSAwesQsRqecQKQjGr1BgZKAmAAosk7OsHK2FO3AZhcYP+FbcuWzKHLqVFaaecy/hUVj03nkvWfYMvF3bK3+FqtXY6b8Iya4EtlTXMsPFXFEx/g4Y+y3WCzZ1Ef5KEj9CduEGXzhHkOEA+KcKl6flxhPMTMtxUwXDzJezcfkL6XULOHzJeLV0/CQgCrG4TObUaQIZFQkVUKQRESThksQ0XQXkt9MhK7hs0SgCTpmQQPVjCprmEVAUxA1M4jgE1UEVUNWA0/VillAwB8YSpJEVEFGV1VkXUQwm/AhIuo+JFXEJMn4xSYJ0PzImgldAlUAWfeDX8eCjGrq3G/PItvQUNEEDbEHk9kWk4yo6UgyKIjo6EiahqgKyIKAJgmoghQQY0FEVAV0k4YkiuhCQHgkf5MIIyBIEugamq6i6yKioCIIUsCrQQYCw5roIOsiSKALFsyo6PhRFRk0HROArKNpMmZRC4iXrqOrOhZ7/xcWaxfEccDcjUXZ8FgMQBBExo2ew4ikqUQ0RKI3HhKTlpkZ6pMUVsrbqSqqBnTmzNmLx1PISuu9pKoCZapIFBKZ8alkn2Rj4uY3WJd+BVLSRt7Z8jBnzbqaiXmJjJVPYn9yHZs37sbpOpWhQyFrWB4Txf9xsb+amuhZ7E3+EdmZY/nBOYo9/lF85daR6/1UlTvxVtZyZnwlJ9qLGUshae6vMFXkUxyTzMGodDR/LKqSgEdLIEKIRCISs8WP1xIZ8NJFD6KiI4oCkijgl0RUTUZSXJgVAbtiRTKByyShiz5EXQGviEUHv0kGXUS36KBLCGrgZqzLJjRJR9cEzIKAKmqosjkwj6NryKoJXTaj2gVUXaUoP79zH45JRRDApJgpKinq9mcsiQKiICAIIiZdQBNBEzQESQMVJE1EkkEVTYiijiD6QVGRdSkwhCdLeM0CoqAhKn4EVUMURQTBhC5r+EQLqBoC/oDHg4wJM4JZwK8DgoqmKshawGszCwKKRQIRVE0FSUNQRMyaBrqMLknYbfZuX2+osVgseL1tTxvIsoyitM5kEh0d3elzDBxhMTyW4xZRlJgw5jSGJ0zGVmdHdypQ3FpMAHRBZ/fIOtaW5KCqgazIp5xaj8ezDpc8lK8bI8lSoAzw2lKY79O5d1AOb60ZxFRtHRtKTmDQhEg+W/0Mw4ZOZVbsOYzbF8do02zyMhvYXLWHggIQhB8xbhykpxeR2PAwszUXqmDnYPR57Iqfw4akQewZZCJSj2WDdwS7HX5c1R4Kqxz4/T7mWmo5xVzGROkAmewnuvFjRHcNdfZ4CsyZlJKAxREJ7ih8eiJ+xUwMcciSFbNNpTYqDkeEBVmqB9WHoEqIioaky2hmgUbBhFV3I6gKsseCJIggCSgWAZ8ggeZHVHwIClhUM4Is4beI+HTQBA8mH5h9AUF7+vnXO/U5SYgImgSSzr9feLnbn7cgga6JiIqEIAfmNHyY0EUNdBVJ0BE1ERkdTRLwSxZ0k45fVzApGroiYhMkBEHCL5kQTDqq4AdVRdTA4tcRZQFNsOI3CUiSiqoqiJqIRQ0sclAFGc0sgqDj11VkXUFXQRJNSGjoJh0fYtOiIT/I3Y+LaitWpbu0JyoAo0ePZufOna22n3nmmZ0+x4C5G1siIsJtgkEvIklmssbOZVjcRKw1FnS32qaYHMIVo/F97F4OFBU3b8uaqAGBfFYbIq/DV69ja/rdOczJnJG/mftSY/nv2XYWPfEy4y8Zxs6i0WRMvITCne9xoGgHZ826mpiKWEbtj2G4dAIHhjrZ7NjDzp117NyZgcWSwYQsPwkJuQx1vkem/jYLAKdpBHn2hWxlCuujoqlKlBBH2xmqilS6hrC0bixPlzsornEDMDHKwZmRFUyniCmuAhIdOcj1BQjo+EUTRQkZFMSmUyZHITvtWBwW/Foism7DrtqIFmJQzWb0GI3GKBMNUgSS7sYvexE8TlAlJB9YNTsSOqpsQbVIKIKC5Peie2UsuoAkWMHkw22xoGoC+/MLO/WZiZqGrIoIJhO5nWwTDEk0owsSqsmPgB9dlZABkyIgSCIe0YSmaqiyjqQF7umSLiAgo0oimiiiaSqSqCLoAoIKZt2EICoooglVBk3XEDQFGROCKiHrMrqookkCPkRMgoogKEh+AR0ZSRADK8kkDUUwoek6EgqCqiKoMmZL9z2W7777rsttbDYbbrc76D6r1YrH03ZVy8zMzKDCEh8f3+nzDxhhsUfHhtuEPk2N00V8RN9xx7uDyWRl4tjTyYyegKXGjO5SwQU6HddjOTDMybc1W/CWH35aS0sTSEz8EFXV0ZD4xD0a0BDdAXGqkRIZ66phRsx03tf3sHDOZFLeuYvGy/5N0cHBpI25gorc/x32XhLPQar2Myw3kkxxGgczXWzx7KeytorNm0zAWGJixjN+vIPIyB3g3c3E+iVMBC5HoNI2j92WM8lWR7DRJOOLNkNGPHE6DPcI2BoVllcN4umCkXj8gSfgZIu/aSithHFiIbNr92Gr24CgHL5xVEckUpCQSX5UHA16BJZGE+Z8EUmJx0ocFsUUGGazROGxmyDah8cu4ZJN+BExiy6cugez24uoyIgeGavPTIzgQbB0rhYOgGaz4dV9CGrwG15n8UoCsu5F9gZWbUlIiJKEYtHwiDY03QeSH8kf8FzMmg66GVHWUc0yWlNMml/wI/pFBE1D0sRA7Iuo4xVtqIKKLvkQVA9oMqJfRFYELBKYZfBiRRdAsbgRNQ2/Hhj+kv0SVl1FNOu4sYCgoVr92Kzd/+0tXbq0y226u6QYAuljli9f3mr76NGjO93HgBEWi92OJMuoQcYG+xOqpvHoF6u4ZUHHRab2lleRW1FFhNlMg8fLvLEjsFvM7CqtoKimDqssc+qY4RRU1SII9EthsVgjmDRmHkMix2GuktAdGjg6JyYAPqvO+sEH2H1wf4vtVitMyPoWr9cBQF7UYoodgR+jr6m2TznxjAXO8sFa4JHZVfxtg5kRy+7EMf9BqksTic/8GfUl/yO/YAsHinZw5qyriK2IQ1QhIz+CIcIkSjO8bFH3U1pVTn29xpo1duAEUlNnMnp0LSbzFny+gyS7vyLZ/RWnAn4xjsLIn7BTmsV6bwqbBA1sEiRHYhoXyXBFINGpodV6+ao8ijdL04FAPimTqHNKXC2nRZcxWT5Apj+XqZV7mH6gZVoQv2SmMHEo+TEpbLdF4fGZsDXI2It17P54okjGpJmQVRGrPR7NGoHbJiJHOPHZ/dSZo3B2EMNy5PovRbVjkkz4LB0LiyiKbd4cI/0qqiTjt5gQBQ0NL5rqxeQ3I+sezAjogg3d5MMnSdTqViyqG0HwIiluTD4zZkEG7AgmBcUu4EQGQUPXFGS1AYs/MAkv6xYE0QxmH4oo04iASVJRNDeST8GkiYiqjiCYkJDBouCVLPgEAUH3oul+ZJ+IqQerwlat6noBw/bmUdrzVqKjo5k3bx733ntvq33HZ0oXwBYdg6MmtOORvc2G/IM0ejqOx3F6fXyzO5dfnTYLgHq3hw+yd3L5rCmsyS3k6jkn8PqaLfhVlQM1dZw6etixNj1k2O3RTBxzBoPto5ErRWjQoIFA4GAXqBzs5WtPDo0HG4/ao3PKKfvxeA8Px6zg7Ob/NzQEsrsWN9VrObNgCw+kRLHdVE7hj6eRsXQ9WZv/zfpxN1BfGU1k8hXIlndxVFfy+epnGZY5hVlJ56JX+xF0gfRCK+lkUT5kFNlCPkUVgaG4sjKdsrJY4HRGjIDMoWXo+kYUpRaTVsvIxpcYyUssBBosk9hnO4ccLYsNLjt7BI09sQLEWmGYlRRNIMMD1gY/dZUuVpWLfF0dD4xvvq7xkU7OiK9ghuUgI7V8kpx7GVGxn5Hlh8tZN7930SkUxGeQHxFLgcmM7paw1wnEVUL8wVgsYgqJmp2kDj6SKSfNZMvq9QA0xpZhcXqQ3O2LERyRKSEIZp+IbPbgNIs4iMCqm1GtXvy6B4tPw4MJFC8Wr4yMRILgRDPreEQ7iiAgyQoeXUFU/Qi6CF6BCFR0QQNJxydF4JIkLJoHr+RH8PuRfBI6CpGCAIKMTdLwmy24BAu6JmDRnaA7A16SDyKaht6QRESTQmRUVIfX3Ob1mrseXBks7X1nSE5OxmZr+fmYTCb8fn+r7e0xoITFHh3br4Wl2uHCapaxyB1/LAVVtaTFHl6lEWOzUtHgwOtX8CuBp3lRFGhwe7H0gxVzkZFxTBp9BunWkUiVQJ0OdUAXxQRAlXW2DqtgU9H2oPtPObURj3dt8+t6y2R+cJiaX1fUB56o870xAMQ7q5gePYX19Xu5d/ROXkhOxJS/nalpn7HeOh9HrQ1rxKXEpS+jtqSQ/MJsDhzc2ey9oAae2VOKLJzNWKrSh7PVcoC80sPClpsLubmpSNK5jBunkZpWiN+/EU0LPF1Ge7cy3buV6cBVgkyp/UfsMp3OJn8G21wC5aJOuR2wy5AajTUrmhGKSJxDxV/joajMwU4H7HQMA4YBpwCQYPZzVkIVJ9pLGC8eIN2zD3vdXpIayklqKOeEo947r2ylICXg5eyzRXCgg/vXIVEBGFEwFFX2Idvr22kR+N7KsozPFzxRoifSToMpEZPuQhYbcYo+ZI8XySejaGLgxq5ZkQUd2eTEazLhliLwYcMk+1AFBVVQMXlVVFVEUjUEzYykWBAFjRiTE79JplG0IehWZNmH26pg8nnR1EDMkKQKCKpEFD50ScUvmlFEG4LZjyaqqKqKqKqgi0hqz26zpaWlXW5jMpk6PigI48aNo6qqCgjMxXi9XqxWK36/nyFDhnS6nwElLLYuLIfri2w9WMrpY0fwSc7uTh2vHjVU4FMUqhxOIq0WFFVD0zQO1NQyPj3lWJjbY6Kik5g8+gzSTMMQK3WoPTKXWPdoSFT4xryTiqLKoPuzsjTgoxbb1tiuQWsqQRGng9MbEOa9nsNPmWcpIuuBetHDd+eN5pT/VGFf/QETfzKKnLqheJxmTObzSR72JRX5u1BVXyvv5RCJJWbmMZJpKUPZGlHE3pK85n2qCtu3i2zfPgyrdRhZWT7i4vfi8eRwSGQFXSHduYx0lnEG4JHSyI+8gG3CdDa44ynxaXgE2GHSIE6AOBuMsDFIFRjsAVO9j5oKF4WVTqp9Jv5bmsZ/SQOmAyAJGnPi6pkbXcZk0wGG+vOIadiN5KrEongYU76bMeWHv6MPduJzsZgktH1LsYgpoMcTZbXT6HG1Om7OlJlU1JWxt+BA0H7O+smpVJhKsDRqiF4TZgUiFAW7ZEUT7KiCjNWq4LKbqTLZ8AgCEYILTXTj12vQ3R7MfjOiYkH3S8RoMiZEkHSwuPGabdRJibhFEzbBiSp6cOHF5PWhIiFqfgRVRFKtyBqYZD+ioFIvmdAxowlmBNGHz+RF9nsQVRnZbOnEO9Q2mZmZbN8e/CGpLRoaGrp1rnPOOYfhwwNlumNiYvB6vWiahs1mIyMjo9P9DChhsUfHhNuEbrOjpJwJXRCAjIRYPtm2G5fPj91sYkN+EV5Fxa+qLMgaw/r8Ik4YNoSqRicFVTU0enzMHpERSJgXRmJj05g8eh4pYiZCpQbVgfxTPUUXdPaNaOCH0i2ojuDzL6mpAolJH6Gqh8VLFex86hrcbEOyInLIj9jZGNlcsejMwmweTLKj6RpPJG3l5AmjEHfsI+GDfzDi8kfJLbHg98ko6tmkj4mkZM8GgCbvZTtnzvpFC+8FILZc5lSGMSUxg+2xxewq3t8imarHAxs3moEs4uImMW68A7t9K17vvhbXZVVLGVf/FOOAS4Bq64nssy4gWxvDRqcZd1MwYrGkUxwBRJggPQa7HsNon0CsQ8VV7eFAWSMNbgVVF/m2Jo5va+KAw+PqoyPcnBlXzgm2g4zSCkh27sVUn0eCDaqDTJvMSYHvywP/3/LvE8iPqWKn1U2+mMewgiFsfad1evZT49L578G2Y1xuX3gdJq8HIdZDndVKtWyjTpVRNJ141YGsN+DUVUzOWqJrfMT5rZhUHVlVEIUIRCEJRBNms4IvEuptNhyChCJAFC48ogu/XoPV5Uf1WRD8Vmw+nUjNgkWQUUQJwaQjWjUarXZq5DgUXSdCdILUiEv3YvK4kdwygmJC9etY5Z4Jy4MPPsh5553X6eNNJlObxbo6YsaMGYwbN47f//737N69m5iYGPbs2cPzzz/fpX4GlLDY+qmwePwKjW5vl4Qlymrhp9Oy+GTrLuxmMxMHpWKRJSItFhIi7Zw0MpMap4s6l5sDNfUMT4pn28EyJg9JO4ZXEpyEhCFMGjmPZGEwVChQCXRy8r0zeKI0ViXsp7CdG5LVClkTV+H1tpxv2RX9K6obDgtbjP/wjb3SZ0KPiUbwNpDYWMHUEWewqX4fugAvniFwzU4BdJ2Md/9M4wWPUVGuoqsiNRVzGJIVQdH2bwBQVYXPVz/L0IzJzEo5D6paDvFEV0mcVJXBpLhB7EwsY0fJvuYYm0PU1mqs/sEOzGbQoJMYObIK2bQZn6/1MEmCZw0JnjXMJiCcByMWslOew0ZfOrvdh6/PJcBWiw4WERLsCKNsZKoiaW4dsc5HZYWTompXc/7WvU4be51DgaHAHADiTApe02JwB56Qj5ysPyEzICyyCONKdzKu9PAS1pgqD1uDfE6vNa5h/F9GkXdTaVPW8Jb7rTsq8UZY8VlkJElliFlktGxCl3X8chSq2Y4pwoNoc1IfF0WVyUa9bsLlNxOluYhSHXhx4FUUohtdRJarxGk2TJqOrkIUEViERERBRjdrEOHHEWen1mTBoVsw4ceMA5foQFBqsToVzH47ut+Cxe8lTgObEI0imlAkCavJhy22Z4tm/vGPf3Tp+Mcff5zrrruuzf2SJLX6fh0iMjISgCVLlnTpnEczoISlv3os+VU1SJLIlgOBwjw+VWXLgRIGxUaTHB3ZZrsRyQmMSE5ofu30+YmxHV59sresCpvZRHyEjYQIO2vzDvSasKQkjyBr+GkkamlQpUAF0E6MSXc5OMzFN7Vb8JS1vdIlMFmfi8fbOkL8M3VOi9cWT8sfnM+eisUbuGnOV2UOrav6IiKPi+dNJearzQg+D2O/eRDnrD/jbFAAgcriaWRMiuTA1sM13wsO5FBUvCOo9wIQWSsxs3YQE6NT2ZVexbayPUELMhUXaxQXxwNnMGqUQEZGCZq2AUVtPfwh6S4yHf8lk//yIwKxM7n28wOxM+4oqv2HRVUXBApknYIoIMoMQ8zE6PEM90JUo4KjykNBWSMu3+H3qNrpx3HEe6ZLJlADNjeOvwzWv0WUTUaNSEVyBtK1K5rOqgMqczPg2wOHK0pEmWBkVSWf1bmRBchItlLpVHE4Dr8H9018l3GuWEY5Ismok4ms8mPy2dCkRISoNLyR0bitAj7JhCRbyDRZsUkmFFnDL1lQzWZMkU5Em4OG1BiqTHbqMONSbAiqSoLSiEl14sCP7HUT2+DEXi8Qg404VUDRRATdTjoxiGJiIEVOpBd/pEqjJYpS0YpHN2HXHYg04JG8ZMT27CFqy5YtXTp+0aJF/OEPf2gzjiUrK4ucnJyg++z20KwcHVDC0l89lnFpyS1ef7p1N1Mz0jts5/B4ibQG3Oxqh4uM+FhMTRP1h4ZUNF1HEkVEQcCnhM5LCMag9DGMzzyFeCUlICblcCzEBMBv0dkw5CA7D7Ze0XQ0p5zSiMe7ptX2KvtpbHG2HBrU3S3fI5clCQuBc5xVmMNDCZZD1WN4cFoRf18bge50IpcXMqXwLdYlX4rSdLOuaAqkPLjzPbSmJ8SW3su5gffpKGwNEtMaUphgT2bPkGpyKna3sXRUYN8+2LcvHVk+n/HjNZJT8vH5NqLrwYdCIvy5TKp/lEnAFQhU2s9gt/lMtqjD2eKU8R/lItQLOlusgFWCpAikcXaGKxJJThXqfOxduw5TbCr+igKQRKTIeNT6ckDkI+UE4C0cUcMYUf0oI+xuzoqvILp8I3LsCiL9DdhK63H5IcoCibZAin2fqmOTYXSEn1mJ8O5OsMgQaYJlBVspjhtMQeog8ofHsMEkka/VUuDZT4RTY6InkVGNEQyul0io9GFyCqhyAlpsBkQnodok3LKOHws2s43hshWzKKNIKj6ThGaJQrY7EG1eGmNNVCcNokCy41aseP0y0YqTWJ8bHw14dA8RbicxDW5sDWYidRs2TcaviJiwEUkkohhLoq3zk97BaGtV2NGehyAIREdHY7FYiIiIaFNY2qpdNXTo0C5N0LfHgBKW/uqxHE1z8jrgzbXZzB0znMHxra/tyZVruGzWFDLiY1mXd4BTjlhSnFdZw/CkePyqyr7yKpw+HwmRoY9jCdQymUOcLykwQV0Gx0pMDlGV7mOlP4f6gx1PUE7I0kFYFnTfd+afwVG/PXdjyxtynSmJuKb/JzWUMXX4PDbXB2Ji8uRadp87jTFvB1Y+WXK+YcrZI9nI1Ob2FUcEUvq9h72qw97L1cRWxLfyXgAsLoFJ+xIZaz2Z/UNq2VK1u82bhaLA1q0iMIKIiJGMn+AhNnY3Hs822loQIaCT7FpBsmtFU+xMPIWRP2GHNIv13mQKPK3nvlQE9ska+2IEiLHg3lWHjhPBYkKWZCKT0qh11iJYIjHHp4EgYRs6BYBcl41cVyZ1q9dSv7US/B5MMclQX4Fbk6lWJFJSErl8aTWnZkr4m+6Z8TZIihAYlygi6hpDag4wpOZA07q2wzRaY8hPhIJEgV0WK/mCRoHSQJFzN5n+bUx0JzCy0U56jUhapRtzrR/FnIg3cSi+mEH47WZ8soZPUBAEO1FmGyMlK6Io4DcJ+E06qsmEZPch2H24LSpVcQmUDYnEqVlx+S2YfSoJiguz4sZDDegO5iRlBn3/O8v06dP54osvWm1fuHBhc/DkIZEZMWIEVVVVzSu7jiY+Pj5oVD3AP//5zx7ZeSQDSliiEhLDbUKPaHB7yCkqpd7t5vPtezlxRCYHamqpc7sZTGthmTVsCBvyi8irrCExMoLRKYevv87tbh4m215cTk5RKXPHDA+JncMypzBm0InNtUwOpZ8/1miSzrbhVWw8uK1TFUNTU0WSkpaiqq1vkD4xkc8cCRx9061taOkZVJLAkRFAZ6kmNh/x+sGh23hlcDr6wcAwZvTnLzD20vvYXR7XfMyRgZQex2ExDHgvz5GZMZnZbXgvAGaPwPh98Yw2n0heRgOba3fjcDravG6nU2fDegswmYSEKYwd14jNloPXm9dmGwCTVsPIxhcZyYucT2AZ9n7rOeQwgfVOO41B3kf3Zx8hpKZjmTQNaVAGPsWP7KomaepsZp46Cn3tPKKnzuXQ+i9d13DtXoVl8HgETSHpp3+j6oMH0VQfDZWFfDb7Wao+epiJ08bTuOMbzDEiZ05xMC7ahUU5Oh6pJVGeeiYd3Mqko7argsTBhAwKYu3kp8uskiXyNT/57koafLmM9+UxwR3P8AYbaVWQXunGVOlAMSfgTRmBJz4Dry0Sv1nDJ/jxax7spigiZRtjJEsgm7JJwicLqCYVMcKHYGvEZ3FTZYogKqlnqzLbKjM8bNjhb6YkSSQkJNDQ0NA8TxKMwYMHU1lZSWlpaasg1BNOOHpxefcZUMISm9rx8FFfJtpm5ZTRw1p4Hn8+Z16bx88dO6LNfdMzBzf//+yszqdiaIuRw2YyOn0m0a7YFrVMeovGBIVvrLsoL+pcFTurVWiarA/u1eREX4ezvqWoSOhUNrScqynR41q8PuvANh6ONzUPh7lEP8t/nMyC50qaj0l75280Lnqc4sObWgVSHknhgRwOduC9AMg+gdH7Yxghz6Qgs5HNjXupb2g/JqS6WueH7yOBkxmScQojhlciShvx+zt+H2O8OUz35jAduFIwN8XOzGWTP4PtLlDcLpQDecjDRxP1uz/R+NQjeL9dAaKE7/LFfB+pUF20E8d5P2Lo7MFkeICDFdSYTWiSTPQpP8OR8zn+uhIQZKTIplxUokROznYk2zBKo4bjqyjgndzdzPjZg/gS/My0FTOaQlJce7HU7UfQ2veQJV0lsyqfzKp8js5nUW+PIz8hjvxUjfxMhdUoFPgbOegqR9JKyfJVMN69m2H1FlLLddIrXZjKalFM8fjSR+FJyMQbkYjPrOAX/fg0N6LgJcoUR4ScSjxmUib17IG3vj74Z3woYNFsNrcYKq2pqWmzr5iYGHJzc4HWaV/2799vDIUFw2K3Y4+JxVVfF25T+j+CwJgRsxmVcgKRjqhWtUx6k30j6/mhLBvF2fkhtjmn7G/3Cf1T31SO9laSNZHao27qhb7YFq9T6kuYNPR0chpym7e9mLCdM6eNR94cGGIQNJVRH9+J48wHqa85/H4dHUh5JEd6LyemnIde1fb7LCkCI3KjGSZOp2iok82ufVTXtX0zOUTRAY2iAwkIwtmMHq0zeEgxqroBVW3b+zmEqPsY5PyQQXzImYBHGsRXpZP574gMKgvyECIiibn1biq3ZYPiR0odhCAIJL4RWLxQhk5ZBDAmCbdZR6/az4mXzcN2cALb1Wpqy8vw+lV01Y+m+JEsdszJwxBEmegTzqdhvUqJnMbzxfA8GcCJAETIKmfEVzMnspQs6QCDvfuJqt+D0MYDxdHEuGqZ4qplylHbDyf1NJOfKLB1mMKHqot8TwUNvkYi9XomeV2MdeYztMFCSplKdLkTuaQKRY7CN2Qc3sShNMQOwhzd/SFoh8PRZkzKobmXo6PsX3311Tb7i42NRWoqinj0HM3MmTO7befRdFlYamtruemmmxg6dCg1NTU8/PDDHRaAWb16NZ999hlJSUkUFxdz1VVXMWbMGCAwl/DUU0+haRpbt25l9uzZ/OIXv+je1QBxaYMMYekmzbVMEqcR0RCB7ghey6S38ERqrE7KI+9g1zLhzpnTiNe7us39ByMuYK+rtVeQ6BeoPWrbPnfrVBxnaRaOXlPz5GlubsyRAhGOgNhQzaScp1g3+lp8RywIODqQ8mgOeS9nzLyauMq2vRcAURPIzIskQ5hCcaaHLf79lFd37InoOuzZI7Bnz2BMpsFMmKCSlJyL17sZXe/cg4NVLSbqwB7GRDkZP0XgpB23Yj75Sv4u6ehTZoOuU3ff7URcuhjT6PEt2sb9/UmqrjiPb264BPPk6UTechPef97HqCtvQFizmugpU2morqO+sQpfdRFywmDk2OCrGZ2KxEcVyXxUkQxMbt5+QkwDp8eWM81cxHA1j/jGPciNxUH7CIZJ8zO8Mpfhlbmt9tVEJJKfkEh+vI2CVIkVuCnw1VHsrkDVVeI0L5O9exjjKGW0nIYgXNjp8x5NTk4OFosl6Nza4MGBUYmj77+HPJJgDB48mNNOO41PP/20lbA0NjYSEaIs8V0WlptvvplFixaxYMECli5dyr333sv999/f5vGapnHffffx6aefAuB2u7nooov45JNPgMB66XPOOYcxY8awbdu2bqWIPpK4tEEU797Roz6OJyRJZvzoQ7VMbIFaJmEUk0MUZ7r5pmEL7tKuZcIdP15HED9q95iV8gVBt0f6W88h7HK2Hq+ef3AH/4wVm4fDAH6wFnH5/KkkLd/YvM2Um83U9BWsN53OkamvggVSHomqKnyx5jkyh0zixNSF7XovECivO7jAxmAmUpbhZYueS3Fl59KA+P2QnS0Bo4mMHMOECW6iY3bh8eygoywIFZUKxcV+amtVzJZ1pGzehNxQz98WmLCZ/8dte3NIra+g+oh8Zf79e2h85lGsZ52Dd813uD9dilZXh3nGbPK++hjBHgEJZjRRRqSGGHMsws4PmX7JLZS5ZBzezn0vN9RHs6E+GhgFBIaTB1u9zE+oYJathDEUkOLeh7V2H4LWNS883lkVSPNz1PZDST0LYpLJj42kIB2U+Ejmd6n3luTl5RETE0NdXV2rfdOnByw4ugBXbm5um8W6fvWrX3HRRRehaRpDhgxpnr8RBKFLucA6okvC4nA4WLFiBS+88AIACxYs4Prrr+fee+9FFMWgbaqrq1tMtNpstubsmoqi8Pnnn/OHP/wBgIkTJzJx4sRuXcgh4tMH9aj98YAsm8kaM5ehXahl0lsoZp2NGcVsP9g6KrsjUlIEUlI+RAkyyXyIQ8W8gt00ZXfr5dh5biu63YKgHpFuv7aIrIy5bGtoOdR238RcHl8djV5/eOgiYtW7ZF04km01LceudVWkumIOQ7IiKdq+MqithUVbOViykzNmXkVcZUK73sshUg9Y+BHjqRw0kmxTAYVlna/U6HDorFtnBaaSlDSNsWPrsViz8XqDe4zDh1tYH+Wmrl7l5puTAFixwkFaskKi/x3ee9UO/BunvJxc+/nkMJn/PfF35BFjiP7tbWi1NdT+8df4t29BczmIf+Bx6u79E+ZJU9FqqpAmTsE/eQbuj99l+8wMRF1nqCqS6tIR6nyUHVGrpjMc9Fh4sXgILzIECCRvjZA05sbXcEpUYChtiC8wlCZ66jrd7yFMqo+R5XtbJvWc84cu93Mk27Ztw2KxNCeChEDm5+joaOLiAvN/UUcluJw9e3bQVWRJSUmMHj0aTdOQJAlRFJsn8LOysoiJCd2q2i4JS05ODmPGjGkWEZvNRnR0NHl5eYwcOTJom6SkJKqrq3nhhRe45pprKCsra06/nJ2dTXR0NA8++CB5eXnk5+fz17/+lblz53b7ghIGdz6fzfGE2WRj4rh5ZESNx1Jt6lItk96iJs3P1+pW6g7WdbmtxQITJ/3Q5mT9ITY2FfMKhuoKUnFSF1AjUpAbWuaumq/b2HbUsaVSI9nnTWXy6y29kKT3H2LY5Y+RX9IyMaCAQGXxVDImRbQIpGxhk6rwxZrnO+29NJ+z2MxZjKY2dRg59iL2l3SyfHATlZU6lZXRwKkMHSowbFgFgrgRv//wMtZRo8wUFvpoGrLH7dbQdZ2EhJZJTyP8+5lU/0+GNaq8uauQy86wkRmzny0Rw1nu9SLGJ4KraZ5HlNAaG0GWEWQTSCJ60zJtTRDIk3XyooGjatXYGxUaq9wUlDU216rpDE5V5JPKRD6pTAQOP9ROi2lkXkw50ywHGaHmkeDYg9zQjXLKyZ1PNR+Ms88+m8cffxyLxdIsLJqmYTabOemkk4DW6exHjBiBqqp8+OGH/OQnP2l+sL/77rspLy+nsLCQe++9l82bN1NQUIAgCLz//vs9svNouiQspaWlJCQktNgWFxdHRUVFm8IC8MEHH3DKKafw6quvMnXqVB555BEgsIxuzZo13HTTTdx+++1UVVVx1llnsXnz5m6nfU7M6Nma8YGExRrB5DFnMDhybKCWSaMGjX1LTAA0UWfHiGrWH9zaqWXEwTjl1Dy83rbHlgE0JD5uKuYVDEdj8KBCtzWZqKOE5ayDO/hnkAe8fwzeyuvDMyCv5fGZ7/+FxoWPUlXRWryCBVIezSHvZd7Mq4jvpPcCEFdmYi7DmZaUwdboYvaU5Hb5PS4o0CkoSEIQFjBuHKSlF6EqG4iKcjFlio3NmwOLiTesd3HWWVHoOjxwfzkXXxLL6NGH82RFRUncelsS2dnbWFx3G1MbVPZGV0OEjJ44lBhRpaa6AkaNwXrafJTcvegN9Uhpba9UqhVgk01vrlUjj4tguCKS5NTQ6nyUlDkor28vK0NwNtdHsbk+ChgJzAUgzepjfnwls+3FjKWAVPd+rHV7EdR28nIlje3yuY9k2rRpAJx44ol8+eWXyLKMKIps376d5ORkzjvvPN59990WbQ4lrPz5z3/eYvucOXPYtWsX06ZN46WXXiIuLg5RFBk7dmybKV66S5eExefztfpSaprW4eT9k08+ydtvv01ubi433ngjc+fO5YILLsDtdjNp0qRm5U1MTGTIkCHs27evS9XKjiQ6MRmLPQKvy9mt9v0duz2aSWPOZLB9FFIPapn0Fo54lW/tuyktKut2HyfPceD1/tDhcUcW8wpGVRs3oAZTEkdP4Q+qOcCEIaexo6GlJ+ATVN79UTQXPdnyeNHjZPz3f2fj9NtwNQYRlzYCKY9EVRW+XPM8GYOzODHt/DbjXoIRXSkzpzKTKfGD2Z5Qys7ifV2uMqjrAjt30lRuOZPxE/z88ZadXHjBB7z+Wi21dSq//nUCiqKza7eXykqlhbAAnH56FNFREm//t46cHDdDBolcfoUfk2k/mz5YwKDxcRBTQbWezOacDbirK4hYdGWnbVQEgb0mnb2xAsRaYKilVa2agnIH/k4K85GUesy8UjKIVxgEBFZQ2SSVufG1zIkqY6J0gAx/LtH1exDdNSDKkDSmy+c5kri4OM4+++zm2BRFURg+fDj19fUIgtDqQR/g2muv5fPPPyc/Px9RFImMjCQzM7N5quGmm27izTff5IQTTiAmJoYlS5Y0L6YKFV0SlpSUlFZrpOvq6khNTW2zzbZt22hsbGTGjBnMmDGD0aNHc/7557Nw4UKioqKal74dIiEhIehEVVdIGJJJyZ7g0aUDkcjIOCaNOZN0y4ge1zLpTfJGNrKqfAv+Tg7vBGP8eB1R/LBTx64Qzm5zn1WHGkfwJ89qMZFgM3dn6XaCLRP5X/Ruzj1xItY1LQfL5JI8pqS+w7r4C1GV1je2tgIpj+bAwe0cLNnFGbOuIr4qEYL01RaRNRKzawYzKSadncnlbC/dG3SStyO8Xp0tm2VgElbr11y66JdERe3A49kFCLz+ettD0tNnBJbfxsZKnL3gsGSnp5soL/ewaePnWLd8zIKFUZTosUQNc7HZD9td3ftGd6ZWTVuffUe4VYnllYksr0wEspq3T452cEGmlyt7mNkY4JVXXuHaa69FlmXGjh3L+vXr0XUdu91OVlZWq+MzMzNJT0/nvPPO41//+heLFi0iOzu7ef8vf/lLXnrpJb777jtkWeaMM87osY1H0yVhmTZtGjt27EDXdQRBwOl0omkaaWltJzbcvXs348cfXhUydepU0tLSqK2tJSsriz17Wk7S1tfXNy+j6y5JGQNfWGKik5k05gxS5aGBWiY1Pa9l0lt4IzRWp+STe7CgR/0kJQmkpH6EonR8u6m3TOaHxraLH6WqIm35TGV6XKtoboD5xTt5rI0SQI+eVMufN5oCy66OwLp5BZN/PIrNSusbAhwOpDRZ3qOxnaXDmqby5ZoXAt5L+vlQ2TVxsNeLzKhPIysyhd1DqthavqfbqdY1DdausQEzSE2dyajRNZjN2fh8weck9u71UlmlsmBB66XcX3/tYFC6ibRUE4PSYcuyVVw4eydnAR55MHn289kuTmedO44yX/cenDpbq0bVuv97ymmIZITccy9g5cqVfPPNNzgcDhRFISUlhV/96lc8//zzJCcnB52CqK2txWw288MPP1BfX4/ZbKa8vByv14vFYuGPf/wjpaWl/O9//6Ouro4777yTa6+9lkGDQrfwqUvCEhcXx7x581i5ciXz5s1j+fLlLF68GF3Xufzyy/njH//YvATuEDNnzuTBBw+XAvJ6vdjtdhITE0lMTCQhIYG1a9cye/ZsFEXBarWSnt6zCPqkzP5ThrcrxMelM3HUPFKkDIQKFaqgr3slR1Oa4eEbxxacJa2LPHUFi0Vg8pTVeL3tR54f4shiXsGIV/Q2haVIiQ26fUh1IeMGncKuxtarprLNZRT/eCqDPmy9nDj206cZs+hB9pQFV6VAIOUlQQMpj6Yn3guA1SEyZV8y422J7B1SS3bl7nZrotfW1pKbm8v333/P73//+1b7y8q0pnLLcxk+XGDo0DKuv/418vPd2GwCqqrjcGq88koG69e52LDRRUKCxKJFcezY7sbh0FBVkE0Coijg9R7+fluVg4xveJLxNNWdsZ3MXssCtqij2eQy4emBEHS2Vk1XyBrU81VWv/3tb9m2bRuCIPDRRx+xYsUK3nzzTXRdJy8veADwzTffzKWXXsqll17K0qVL2bhxI2lpadTV1ZGSEkgvk5aWxo033siNN97YYxuD0eU4lieeeIJbbrmFH374gYqKCh555BF8Ph/r1q3j4MGDrYQlMzOTn/70pzz88MPExMRQUlLCU0891bz/rbfe4q9//SsbNmygoaGBBx54oMcXlT5mfMcH9RMSEzOYOOJ0khkceCoNcS2T3kI16WweWkpOUeugwO5wyqn5eL37O3duwdaimFcwbEESLh4i19v2DeIsIZK2rui+sXt49od4tKrWUfHp79xB4yWPU1Ia/GZ4OJByBRX57XvfPfVeAMxukax9CYyxnERuRh1bqnfjdLUW/7i4OGbMmMGqVauOOL/WPIqh6zrvv/8+J554IpBOXl4KFssYXnzxxySnFPLcc8uxWhSSk2X+/XgVGZkm9u/z4vNpfP21k59cEI3Ho7Nli5uGBpW09La9zAT3D5zo/oETAUWI5GDEQnbIJ7PRl8Zed8+8967WqgnGpME9F5azzjqLc889l2XLljFmzJjmUA+fzxc07sThcPDFF1/w1ltvcdFFFzWHhCQkJJCUlNRjezpLl4UlNjY2aDWxttQTAm/OWWedFXTf0KFDef3117tqRrskDsnEGhWNp7F75TnDTWrKCCYMP41E9djWMuktalP8rBS2UVN0dFx79zjpZAde76qOD2zi6GJewRA9bYv1blfbSf3mF+/h8dYjOgBUiy5+OHcyJ77cWlgEVWHU53fjOO1eGmqDzzEFAinnkz42gpLdrT2foznkvcybdRUJ3fBeAExegbH74hhlmk1+ZiOb6/fQEOR3JAgCNTU17Ny5k8bGRr744gsmTpxIcnIyBw8epKGhgfT0dFRVZfbsE9m5U+KDDxTee68eu93Mxx+XUlvr5sABmXHjrFRVqXi9GkOGBNKUrP7BybffOLnk0s7dnGXdwVDHmwzlTc4BnKbR7LcvZCuTWO+KoqYTw6Xt0dVaNbIohMRj+fOf/0xWVhaiKPLOO+8wf/58GhoaiIqK4rLLLms1SpSTk8PYsWPRdR1JkrDZbFitVjIzM9uMNTwWDKhcYYcQBIFBY8aTu3FtuE3pNIFaJqcSryQHxKQX0s8fazRRZ9eIGtYVb+3yCqS2GDcOZOmjLs0mfa4enWC9NV5H2wsIdjns6CYBIchZM6vyGJ02h72O4DXaH0vNYfbYEQi7Wy+FlmrKmbTzedYP+wW+NoRNV0Wqy9sPpDwSTVNZ0UPvBUDyC4zcH81waQaFmU42O/dSW9/ywSA+Pp45c+YwZ07LYmlHDq9IkkRiYiAJ46hRo5g6dRoLFiwAYNmy97jnnvksWfImu3d7+cUv4pvb/d+V8fSECP9eJtc/wmTgCiQq7GeyxzyPzcpwclxSq7oz3aG9WjXpSFhNUod9dERSUhJ33HEHU6dOJTo6milTpvDGG2+0OUpUWlpKYmIiN998M48//jiyLOPxeNqtKHksGJDCAjB43IQ+LywZg7MYl3Eycd4k9JreqWXSWzhjNb6L2k1xUedSi3SGpCSR1LQPUbpQsKzKfhqbnR3HRDU0BiukFcCtSugxiQiuyqD754vRtFVuTBfglfkyV+4Ovt+8ZwNT0kayQZjT5rBKZwIpj+aw93IlCVVJ3fJeAERVYFheJJniVIoz3Wz27qOyJnitj47YvXt3i4U5c+bM4+mn95CaegZ5ubWYTdDYuIezF2iIYvfi2IIhopLq+pxU1+ecRqBkQkHUT9gpzmS9J4lCb2geeo6sVXPN4J4J45HccMMN3HDDDbz66qts3bq1eZI92CjRoZCQE044oTkN/iuvvNLuAqtjQe/5Rr3M4HHBV92Em2FDp/Gjk3/Doml/5UTTOcSWxgZEpQ9wsL77sSRHkj/CwXvaD53OV9UZzGaYMmU1ilLXpXbfmX/WqeMq6toPovPa2q6pMb+k/fQzn0bk0jh3apv7I795iwkJHb9XhwIpRalzT8IB7+U/rHYtg6SePUOKmsCQfDsLSyfx45STW4UJdIZNmzYxYsThUg+H5msSExNxOiW+/LKe0tKZfPThdEymM5HlY1O4z6xVMbrhBX5S9yse8FzAU+b7uClmC6dHeYmUQiNoJ8SEJpnjkSxevJiXX365zWJv0L2QkGPBgPVYkocNx2yz4WvnQ+gtRg2fyai0mUQ7Y9Hr/T1OP+9XFRa8dDVfXdN2euxDXPD6b9hbVYDdFJjoc/pcrPjFy+ypzCe7dBeRlgh+ecIlbCrejoDA4JjufwF9do21qYXsLW6/oFR3OPW0QjzefV2zp41iXkcTp9GijnswGi0p2NgedN+wylxGpp7MfkfbKT8eOqGEB9bZ0Nv4Pia/ex+Zl/+LwpL2f5KdCaQ8mqLiHbxTurvH3gsEEl6mF1qxK2YWJs0hW8znQHnHWYNdLhd1dXVBJ5xzc3Ox2WzExsYSFxfPxo2bSEiYgCwvZNx4jZSUfPz+TWha215lT4jxbmGGdwszgKsFMyX2c9hlOo2N/iHs7GbszEmxbc/LdYbs7Gw+++wzXn/9dU488UR+85vfMHVq4OGkrYn722+/nVdeeYXy8nIGDRrEoEGDWLlyZYchIceCAeuxiKJE+uie5enpNoLA2JEnc+7Jv2PRpL8wTT+dqJKIgKiEgP9t+5QKZ8f1NwDGJA5j240fs+H691h73f+4bvblpEen8Gr2B9w05yqyS3fhUbxsKdnJtEETum1T2RAv79vWs7ck9KJy0klOvN6uZ73Oib4OZycirFPUjn8GtVLrCOcjmS+1/3S9T65m/7ntJ1gdtvQvJHTCszgUSGmNbCOIJgih9F4OkVxkYX7hWC6IO5Xhae2nUioqKgqakv1QJo8jEyMeyomlKLBtq8iKL0ewYf2leNyXYrVO4VjetkTdx2DnUs6q+z23O3/K89KN/DlqJQujG0g1d+68o+wWksxtr2brDH/+85/5xS9+QWVlJffffz/XXXcde/bsISsrq3niftOmTS3aDBkyhJKSEi677DJee+011q9f3xwS0t0UWd1lwHosEBgOK8jZ3PGBIUAUJcaNmsOIxKnYj2EtkwN1JURbIok0d1w8yK8qXHPCJYhC4Afxwc4VnDtmLgAef+DpTxYlyh3VRHSiv2CoJp0tQ8vILjo2AaljxoAsf9it0M9gxbyCEe3r+JhyEmgv3G1+yX6e6mD04/4R23kpLQW9tDzoftHtYMLaR9kw+Q+4He1/bzobSHk0zd7LzCtJqO6+9+LXlOYlxnElEu8te4PLF1yMOs7O3uIgNUxqaoLe3AoLC8nMzMTv95Ofn4/L5WrO2nskLpfOhg1mYCIJCZMZO7YBm31rh/nheopVOciEhieYAFwKVNnmNMXOjGJzG7EzPfVWamtr+e6774iMjOTf//43L730Erm5uTz99NPtTtzLcuB2HiwkpLcZ8MJyLJEkmQlj5jIsfhK2Whu669jXMvl0z7dcO+syHvjmmQ6PNUkyIxMCT5KarrGtbA8/zQqkNUmwx+FT/fhVhS0lOzlz5EldtqUuWeEbaTtVRdVdbtsZEhMF0gct69Jk/SEORvwkaDGvYFi8HfdfrMa2u39ExV6GZ51InrPtYSGH4OPLc9M48/ngwgIgF+1hSupS1kUvROvA2+pKIOWRaJrKirX/YfCg8Zw86IIurRwrqC3mkz0rKXdUcc/XT3LBhLMYnTiU7JKdLCys5mzzeKYmDGZbXCm7S/Y3rwa0Wq2tarHX1dXR0NDA0KFDgcDk/o4dOzj55JPbtaG6WuOHHyKBkxg85GRGjqhClDbh97f9voaKRPf3JLq/5yQCsTNFkeezUzqZDb5U9jXFzpwa38b6804SFxfHJ598gtVq5ZJLLqGmpoYPPviAxx57rPmYYBP3uq5z++23/3975x0eVZn2/8+Znt57JgGSEEroHaSIUgSRVdeKumJbXHfXRddVfN193VdX0HX1t8gqrO7quhYUUBErVQGlRkInQHolPZkk0+f8/ggZCGkzkykhnM91zQUz5zxlJjPnPs9zly86nY7y8nL+8Ic/kJaW1qO5uIogulpO9jLAajGzavHtWEzu25tVKFRkDLqafiEZaGrPa5l4iS1nf2BAmJaUiCQmvXErex7+2OG2X5/eidlq5obBLXWBCupK2Zl3AG1IHPl1xcQERFDVXMeikQvsK5zOEAWRU6l17CnJclsY8aWoVDB9+j4Mxs7irbrmvyHv8k2DYw7UKcVmMo93fdf/UGIBT1ct6/KcVSPns6b+0mL67fnwq1TkhzsJEztP7fW/4VCjY5VxlSoLfv7dJ1J2hEwmZ+b4XxBRHd1u9XL83Bl+KPgJpVzBqcpc7h61kIyYrovDXtzmaH0O46+bgl5h5tSpU5SUlKBWq5k0aRJFRUUIgtDj8k0XEElPF0hMLMFqO4DVqnNTv47TqEonx+9nPDDifoIVPQ81buWPf/wjU6dOZfbsriXDlixZwvPPP09kZCTHjh3j0UcfZdu2bW6bhzP0WR8LgFyhJH5gz+v1qJR+jBk+nxunPMHPU39PetNI1KUKrxqVRmMzlY01pES4pjfzftbnTOt/QdM6OTSeu0ctJDUiCbVcRVbZSRKCo/kq+/su+2kOsbFZm80PRT95zKgATJtW6LJRuSDm5RgGBwoQnm7u3p8xu8yxbZnVV5uhm2S1sC9eIy3OsQrdZpMCnW428YPGOXT+xbSsXv7ND82ft/O9vLjznzw0/jYWj7mZJ6Y+wNPfvtJtfxe3eXrCg3zw5n+5QzaNM8ezmTVrFqWlpVgsFkpKStxoVAAEsrNh27YEdu/6GQ31d6BRT0QQeubrcIZAUzbT2OpWo7JlyxZSUlK6NSoAq1evtucMZWRkUFpaitHomYCH7ujThgVgwOjx3Z/UARpNIONHLuTmKU9y04DfkarLQFUqQ3Rg28QT7C8+jFKuYOOJbWw8sY1ms56NJ7aRU91xYt7F1OrrKaovI1TTfom+M+8AwepAtKFxJIcmkFnSuaxzQf8mNvAjRRWOa4e7wqTJzRhNXRu4rjgYuASTEwvxmvruf3zHG7s3VAPPnaJfQPeF/L73K6B6Vufhx60krHuG2FjHfqIXEimvduj8SykuOcHHB5ZTFVsJCoE6g459RUfQn/fFRQaEUaPvui5bZ200OhnqOrjdOpWowHCMRiMqlcqleTqCxQKHDyvYsiWNzIN3YDLegkYzDPC8AzsiYrrb+srMzKS4uJh7773XofOrqtrmF4WFhfW4Uryr9GkfC0DquIl89+5bDp3r7x/C8PRrLmiZ1NugvndomcxMmdTm+Yrv17BwiGPlrg+WHCPCv71DVBRFREQsNitKmRKZTIbB0j6E1aQR2ZdQSHaJY7W5ekJ6OiiVG7uswdQVLWJe6TgaJCpHpLKh+7DdSpMSMSQIwdj1FsssRThv0r3h/cvwfF79MQhR13l/MouJ9C3/R9PUP6Or6z6i0J5IOSKAwsOOJVJejCja2Lbn3yQkDOaqpJt45+cvolG0GIBafQMR/qFdtg/VBHXaJsI/DEFnIbBOQWKuH2Nn3EyB+RzN+p4VI+2OpqZWueWRREaOYtCgejR+hzEa8z0yXmTEDLf0U1BQwPfff2+Xbc/LyyM2NrZTXfqGhga0Wi2NjY32HCOdTufV+mAX0+dXLCHRsUQl9ev0eFBgBFPG3MbPJy9jQcLDJNelIS8FnJA39QUiIqIoYhNtPPL5nzlS3nmSXl5NMfIO/CZ7iw4zUTuSlAgttfp66vQNJIe2veOuSDTxaeABrxiViAiB+IQvEEXXgx9yg+6mxIly6jE2GRYHq+Ka/bvP8Zld7pgEcLGinqMLuvehyKvLGHH6bZRqx3+qFYXOJVJeSknJST7ev5y0cQkIypZx3zrwMY9dtbjbtpOSRtqjvy5u88S0B/joyFfcOmweOp2Ohl1FmHee46r40QQF9CyKylGqqkR27w5m65apFBbcg0J+HUql+y68GnU8QUGuh+y3Yjab+ec//9mmevSaNWtQq9UdhhkDBAQEMHnyZLtRycnJsdcY8wV9fsUCkDJuIpWF+fbnISExDB/YqmViO69l0rsNSSvnGqv44tQOynSVvLzrX9w1aiFZpSco01UwPLZjf1KwJpCogPYlJsp0FUxKGgnAt6d3s+nUdpaMvwMAq0LkcP8KfirqOCnQ3SiVMHrMPgyGnhWq3CrMder8CDM4lhEETepoVHSdpDmo/ARJQydQ2Nx9Jv2LSUd4r18iYn5xl+epTuw5X/ZlosOSOxXFicQNWkTFWccTKS9GFG1s29uyerE1JZIclsD0/o5vK+/MO9CmTatPr7i+nDJdBVllJ5mgHcHZr7L4ecYMCvo18lNDNnUNjskg9JSCApGCgkgEYS6DBokkJBRjsR7AanVdeTYyqmeCWVlZWWzfvp3Tp0/zzjvv8NZbb6FUKjGbzQQHB/Pss8+2CzPesWMHx48fx2w2ExISwosvvkhQUBA5OTnceuutrFq1CrPZTHZ2NitWrCA0NLRHc3SUPh0V1sq53LN89de/Mix1JjEyLUKl9XKxIz6hIcrCDuVxl2tCucK11xZhNH3Xoz4a1MN5xPS/Tv1pJ+vgpx8d8xntSP2Y/sWfdXve/xs1n3/VdR8dBnBn/WB+9rpj55675X85Xhnt0LmthEQ1dKtI2RXFNfWU1et46o4/ElkT69BK/kh5Nicrcrht+Lx2xz7I2kSIJohaQz2TtKN4L+tz/veaXwMtRUuL+jVzSH+GqlrPhLB3hUoFQ4daiIw8i9F0yOmV86hR7xEeNqn7Ezth3rx5fPXVVwBUVFRwww03sHdv1/UOMzIy2um1rFy5sttjnqbPb4UBxAxIZe6AB4itjEc4JxmVzhAFkey0OjY07/aqUZk4Sd9jowLwo98DTv9p5U5E9lUJjhUWnH0u3+E+Pwg5iXG8Y/lW0ev/j6R4595hfWUwAVGLCIpwziAB1DQ1k1NZzbj+iWzb+zaflLyDPqzrz6u4vpy9hVl2o1JYV2p35nfn05PZBJJzA1hYPoLrYicT48Kce4LJBIcOKdiyZRCHDt2J2fRzNBrHtJ2UynDCQl0LFIILSZGtdcCio6PbOeM7Yt68eXZp6fT0dPLz8x065mmuCMMC4D8s0tdT6NXog61sTT7DrqJMrFbvRb4NHAgq1Wc97qdFzEvrfLtmx+9KS22OGZYhpcdJdMAf08rfr2oARfe70oIoMuDzZwiPdG4Hu6nOD7nfrYTFd1125WKsNhv7cguZOvCCGutne3aw8ae/8cDmZRypah8KbrZaeP/wJh4Yd4v9tfezPketaAn5dcSnBy31yBLy/VhQMowF0VNIjO6Zoqwr6BpE9u71Y8vmMZzOvgeBG1CrOv9+RUVeiyC4HmZ8cVIktFQqcMTx/tJLL6FUtny+69atY/HixQ4d8zRXjGHxG+6b6IjLgaL+zWwQ9lJQ3nkRRU8QESEjIfHLHjnrWzkZ/BDVLgRc6Lool38phRbHq+3OUjn+fTuoLqXsuu7DjwFkjfVkHPg7mgDnjIuhSYXFupDo/o7dgWeXV7Inp5C/fLGd5zZt49mNWzhaXIbFauHA6Z/YXrEJotvOYWf+Ad47tJEJr9/CuH/czKjXFvLV6e/tCbdlugpSIpLIiBlIk0nPplPbuWlo1/kZMYUa5hYOZmH4VPrFupbD1VPOnRPZuTOErVtnUFx0NwrFbBSKtjcZMTELejzO9OnT7YEPr776Kn/+858dapeXl8eiRYsoKytj4cKFDh/zJFeEj6WV8pcPYqnyfbXj3oJZY+NAYjEnip2rGuwOFAq4euYBDIauM9Ad5W+B6x3SXbmU+N2V1DR1nyAJcEN0BSsbfufQuccShnGHynFHdLQ1kH+8JSDWOBa80DxxAfv9r8PmpM67ILcRFvmDQ4qU3fYlyLh6wj1E1cR16nspri/vUcXsS6mJtXDEv5CzpY5F33kKmQwGDxaJiy9EJhQwadJWhG4qVjjKli1bKCkpcTh/pZVWvZaOaoN1dcwTXFGGpX5zPrrt3r0r761UxpvYYT7coeysN7jm2mJMpu4VER2hym8ajxoe7f7ES/ATQdzseLJnWoCeLdb7HT5/zpCxlOodLxD5ROlIxv3noMPn1yxcSlZ9qsPntyIiEp2QRdGxHZTU1nO2ohq5TKCsXsfklGQSwrpemZ2tqKK8vhGbzUaFrokHZl/PnLS72LFnd4dSDD2pmt0ZDVEWjoSUkF2Sg68vYVOmTGLWrDlu6SszM5MjR464vG0VGRlJUVFRh/kuXR1zN1fMVhiA/yjvOgN7I1a5SFZaBZ/X7vaZUZk4Ue82owKwS323S+3irM6tcM42axDljmeMz1I7d6f+SvxhuMin0R3hG18lNd75FfgFRcrr+fpYNtPTB3BVWn/mZqSzIbP78PJPfzrOlNRkpqUPYHBcNO9s/YqP9y/nrRMfsfTq+90mxdAVwZUKrjqbzG1+0xmWOMhn+RoAw4ePdEs/rUmRrUYlLy+vS1Gv5uZmrr/++nallUwmU5fHvMEVZViUUf6oUzyjSnc50BBh5avYYxwsOuqzu7y0NAGVeqPb+rsg5uU8YU7K44iigDWgcyXJS5lV6dzq2IrIe7M1TrVJXP8/xMS65jTOPxtHXnWDvfZkkEZNkwMXnsFx0djOf3+iggKoaW5GFG2UVeWzu/ETFP6KHkkxOENgjZwJZxO4XTmdUdqh9tLx3iI2NpaYGMe/E53hSlJkU1MTBw4csBvVVr2WkJCQLo95gyvKsAAETPSuklpvQBREzqTW84lxN+ec0O5wN+HhAtqkLxFF90kxHwleQqMDYl4d4eeC1rle4/hFZHjxEWL9nAsa+TzoDE3TRjp8vsxkZNC25wkMcf6C6q8OYsmc5SQNvRulWkOz0USguvsV2fUjBiM/f8E6UlzGuH4t0VKBGjUFRSfJrTjKzqY9XDvoKqfn5Cr+9TLGnInldnEq45KGebQW2cWMGDHCLf1s3ryZ1atXk5SURGJiIjExMWzYsKGN9sqlREVF8dprr/Hiiy/y+uuv2/VaujvmDa4oHwuAaBUpW7Efm847S0Jfow+y8WNEDnnl3Rer9CQtzvpMDAb3CoL9n/8nZOtd+wpPLbNw4IhzGh4/pLxLQsk3Dp//4qj5vOdgsmQrQ8xR/HlVLaLB8Yx547Cp7Iu7E4sT5WwuJiSqgfc3PYM2JID02O6NYXVjM98cy8ZPqeRno4ciEwSqG5vJLq8kItCfKl0TyfH9idYMZlHK/G6lGNyNSSNyWlvD4cps9AbPBOwoFAoee+wx/P09vzK73LjiViyCXCBgXM+XrpcDJf30fCLf63Gj4kgF1ekzStxuVIoDfuayUQEwNjm/cqqRO5cPNaeq1OkxTigryZs/3Kk26qO7GCnud3qsVvYeOk1CwizGDnXMJxIR6M+iiaNIigjly8Mn7a9NTk0mKigAhVzG4ZyT5FZ8z9ryjaD07qVGZRDIOBPBbc2TuEo7hgB/x7R5nGHo0KGSUemEK86wAARMiOvT79ysFtmbWszX5T+2uVuzWq28/vrrDvWxdetWXnnlFfvjzTffBODMmTN899137NmzB2jRMm9sbOyyrwkTDZhM2118N53zneKmHrVvaHBeq6LMwSTJVkYUZRGtcT459y9pJxBinNtGC97yNoNjnC+FUlh5mrrGKkZpFzidSDm2XyIH8osxX6Tyebq8Co1SSXiAH+EBfny59zN26Ta0y3vxBgqTwKAzodzSMIEZieMIDupeV8dRxo4d67a++hp9+PLaOYoQNZpBrjl8ezvVcWY2hmRyrLh9teOsrKxujUArISEhPPbYY/bHgw8+CMDBgweZMWOGw4JNqamgVn/m0nvpCr0iiW1OiHl1xLl654szFnUjUXwpAiKzNM5njtcLBr673nkhrNiP/5dEJ4ar0Z3jTNlhJg1qKd5ZXFZNs3Fup4mUJouVf+06YHfet2I5H33UurNuE0XkMhkyQcBksVJadpqP9r9ARXS511cvAAqLQOrZYH5eM5Zr4ycQHtJeRsIZYmNj0Wqdr/RwpXBFGhaAwD7mxLfJRY6kVfJZ3a4OK8TW1tai0Wgcdmp2Fr5pNpvtx3U6XZf9hYXJSEr+2q3O+lYOBP7KKTGvSwm3CehNzpeuyTU4f8c7q9r57TCAf0QfxTasayngSxFEkdRNfyQ0onvlRKvVwu6TX3D1sJvtr/1w8gtEqx9/33AAQ3D7cismi4WimjpkgkBNUzMVDY3EhgThp2oZL7eyhgFR4UQHBdBsNNFkMhEReH67SBTZse8/Plu9AMisAv1yA7mxYhRzYicRHe5aqacJEyb0aB65ubmsXr2a5GTHV4eiKPKPf/yD1157jQcffJB//etf9mNTpkwhNDSUxMREEhMTCQkJoajIdzl7V0TZ/I5Qp4WiiNBgqXb+rrW30Rhh5Xu/k5QVde6IPnHiBFOmTGHLli0O9SmKIlu3bsVkMlFfX8/s2bOJiIggICAAq9WK1WqluLiYgQM7vvApFDB23EEMBvcXs7Qh50uD42JeHRFthXwX2p1qaq/C2R2jirKIGjScSoOjBfov8OY1Nn55TMAZ5TOZroaMrFVsCJ3FsbxM5DIFpdV5TB26AG1kmv28k8UH2X1iE/tPbwbAarOgUQYwb8wvyD93ipc/PM6aJ5+k6NgOTpZVUFRTh0ah4Gejh7Ih8ygqhRybTWTRhAvlaOr0elKiW3YDjpWc43BRGTPSB7SZX2nZaT4qf4Grx99DdG28T7SPBFFAm+9PojCccq2RQ2IOpZXlDrX19/cnI8OxwqGdMWDAAJYsWcILL7zgcJtXX32V+fPnk56eztGjR9m5c6f9WEZGBrt27UImk2G1WnnxxRd9uqK6Yg2LIAgETIij/ivfloboKTmpOnafO4S5C0d0dnY26ekda7V0Rnl5OTNnzsTf35+Kigq+/PJL7rnnHmbOnMmhQ4cYOXIkNTU1FBUV0dTUxJgxY+x1jgCmTS/FYOhc5rgn5AbdTXFjzy5GIS4GBZ5o8kdUCgiOCqMAMtHGNX6JrHXBsGzzy+e2a0YTurV9HkNXqHKOsK0pk0UL/oloA52+ljXf/JHf37jKfk5G8kReuvezDtvPGnkHn+9/63wiZQD/3v0s9101lvf2HOK21GQa9EamdZDMOSb5wvbdnIwuVlvnVy9xsWlM63crnOt5vThXEESBuEINcQylIjGVLHk+hee61scZPXq0vbhjT3E0sdNisfDtt9/aFSWHDRvGsGHDgJZdhKVLl9r7+uCDD7jllls67csbXLFbYQABY2MQVJfnR2AIsLG9fw47ivfbt6c6wmg00tjYSGSkc0v+66+/3h7xEh0djU6nw2KxEBYWxtixY4mMjEShUFBSUkJISAgnTlyI+Bo/3oDZvM21N+YA24Sel89QuZDDAqC3yhH9nffPza527G64I14YVYAQ4FxUU73VyqGSXNL9W+rABfmF0WhwrHZZVUMZ/upANMqWv39F0UBkmghkcjkymUCD3oha4Xol34spKz/DR/teoCK6zCe+l4uJLlYzuyCdG8OmMSCuX4fnyOXyHm+DuUJWVhbBwcEsX76cBx98kGuvvZbvvvsOAKVSyaBBLWqkNpuNzMxM0tLSuujN81yeV1U3IfNXEjD+8vO1lCUZ+ES1j9yy/G7PLSwsRC6Xc+zYMY4dO4bZbObYsWPdaj00N7fVItdoNBguyqvIyclBo9EQGhpKWFiYPYErJQU0fp85/Z4cpUE9nN26nie/2VwINW7F6ERJ/FbGFP5EhNo1h3G+oo6TC5wrjRIil7M6UUvcplcZEG+iydBAoMaxrOus3J2MTpnR5jW1LJao1NsQESisqWVIvBtD9kWRHfveZWfDesQY9xisnhBRpmRmXgo/D55OekJKm5X4yJEjCQpyfju0p+Tn57Nnzx6mT5/Om2++ydq1a1m6dGm7ChobN25k0iTXxcbcxRVtWACCpiWC4vL4GCwqkX1pJXxZ8QPN+ubuGwBpaWmMHDmSjIwMMjIyUCqVZGRkdLmCMRqNvPrqq23qDJlMJgLO3zXbI39sNuRyOTKZDLPZTGioQHK/bzzirG9ljwtiXh2h74FhaVQ5X3NOJtq4xt/5KK9WXuh3BEHrXHTZOH9/BEEgecPT7Mv9hPlj7+22zdH8HxnWb3K71xeMW8wX3x3g2sn3YRbk5FfV8OPZgnbRYT2hrPwMH+9bzrmoMgQfr14AQisUTM3px61+0xmaOBC5XM6UKVN8Mhe9Xs/w4cOZPLnlbxMZGYlWq+XMmbaVydesWcPs2V1LEXgD3//1fIw8WEXA2N6fMFkba2Zj2CGOFvW8zLwoioiiyIYNGygtbR+xpFQq0Wq19j3bmpoaoqOj7XduBQUFJCcnExERgV6vp7m5mYiIMMaNz8Rsruzx/DrDKvjxpQtiXh1R40Kosb2t3LVQ9dm1rpfTMQgWvpznmqbQnpoqRlUcYuzQiV2PYWqmQV9LTGj7zzgyOJ6pQxYQJAzCP2Qy5XozYQF+HC12fYuvQ0SR7/a/y3cN63rF6gUgqEbOpLNaHhh6M+HhzuUxuW0OQUHI5W0/j4iIiDbJydXV1eTl5REW1rNQandwxRsWgKAZiSB3XsvDG9hkIsfSqvm0YTe19Y5pdXSETqdj7969NDQ0sGPHDnQ6HcXFxTQ0tK9wLJPJuOaaa9i3bx8HDhzgwIEDzJ071368oaGByMhI4uLiMBqNHD9+nCUPx3rMWd/KyeAHXRLzuhSFKFLpQnJkKxW4ZljG5v9EuDrU5XHfCT+OeaxzW2LHDQbKLWZuxsKIwrXIu1gJ5JQfRS5TcPDsdg6e3Y7JYuDg2e2cq7sQtnqy+CByaxjRETMY0D+VgmrXv5NdUV5+9vzqpbRXrF4QIPKqfj4bPiMjg+zstrlp9fX1bXLIfvzxR6Kje0cF9ys2KuxiFKEaAkbH0HTAzXdfPaQpzMr3gdmUFpX1uK+goCAmTpzIxIkX7loffbRzDZOEhAQSEtrnMQAMH36h3MjVV1/N+PEm1JqPejzH7thsneaWfmJsMqqdFMi6mBKra3eEctHKTH8t6411Lo+9amoTS7MUYOk+iqrEbOZAczP3nr/Lrtr/LYPmJXOccR2ePzSprVP6831vMTZ1pv1560rXarNis2iwibOQ+7u3TE8bRJHv9v+X2NhUpvW7FeGc9ySzL8VvWCTKaPeXbzGbzYiiiCAI2Gw2Fi1axO9//3vGjBnT5rzU1FQiIiLYu3cvEydOxGKxoNFoiI+/sD165syZdqsaX9ELbgV6B0HXaHvVqiUvpZH1lh8orey5UfEkAwYIaPw+9fg4VX7TyHRBIbIjInsY2Zpvcr0syKzanm0V7tEUUzGnexljsyiyrq6Oey7aFvm4vo6oLW+z4Yc/UVjZXrO+lRpdSz6UiNjGOXy27Ahp8SOICdXSZGygXtdMiN944gd1bKjcRevqpdxXqxcBgme6VxY5JyeHFStWUFpayuOPP05mZmaXlYwBPvzwQ1atWmWvWnxpDkxoaCixse5T6+wJXq1unJuby+bNm1m+fDkFBQU9bjNlyhSOHz9OYGBLaQ+dTsexY8dcTgyq3XiWpj2+vZCb/G3sic3njI+lVx0hJERgzNhvMJs9X4r/09A3WV/vnv3tyTr46UfHlSMvZWZELf9uesSlthaZgqvTBlNncly2+FIC8gRmvF2P3GDgjNHIbaFhDNG01XH5vrGRp8pK0Zz3k1lEERvwYHg4q6qrmT7mBuaP/g3HC/dRUJGNRuXPzOE/J7f8OE3GBqoaSvlkz2rmjLqTq4ZcT2hAFPtPb2H8wFkAfHngHYwWPdeOuI0g/zC7IqWniY1JYVr/27y6evEbFknEosFeG68v4JOy+UlJSRQWOldxt6M2v/zlL3njjTfaZJs+/fTTLs/L2mCi/K8HEH2QCQxwLsnAjqYsGpuafDK+M8jlMPOaLAwG50rCu4JZFs6vZW+5rLtyKdMqrOw/5Pq2Z4zaxD7hXpfb/+/o+XxS6/rnlv9KPmtuvZFR/z1AtcXCIyXFrE3u12Wbd2pqmB4YQH+VmtNGAwdEGYPufIcXP3iSJXOf5+2tz3PXjD+w+8Qmrh5+c5d9dUa09gyFR79wqkqASwgC08fdRVxdoud/qzKBmKWjUUZJVYydwSdbYa7IiF7axhPZpvJglU+EwKxKkQNpZWyq+OGyMCoA02eUe8WoABwO/pXbjArQZZUCRzhnVCGqXC+AOavO+QrErVibrDRnN/Ni1CFISSZCoaDW2vXdu0UU+aGpif4qNQAD1RoWaVQMO/IGFltLEINMJqe+uQq10jkFy4upKEpDO+xWZJ7e5xdFvt//X76r/9jjkWMB42Mlo+ICl62PxVPZpkEztMj8vRfTUBdj4fOILA4XedAJ6mbGjjNhNjtWc8wVzp1re+H/yjTarf03NvZc5M3sQpJkKxPyMwlWuZZkJw+Qk7w0GatK5OO5gdRZrYR3cyE/ZTQQIJPxz+pq/lRexn1FhexvbkJ19hBJKhNWmxmrzUJ+xSkyknuWXFdZlEDcoEUo1a4bKEcpP5dz3vdS4pEKGoJaTvC17vWtXClctoblYtyZbSoPUBI82/GKo64iCiIn02r5RLeL6jrna0j5iv79Bfz9W5z1FovI/fc5VkH1rbdquP22AvvjkV+1+Dj272vm3XdrWb++DoATxw1UV1+4Ay8JWNgjMa+OqKp3PdS4lWaN62GdSpuZqwNc/44FDApAEATWB2fzrwAFv47sOr+lxGzmsEHPOH8//i82jr/FxbOiogJRFHnc1kxB4YdMTJ9Lk6GB3PLj7DqxCZvo+hZTdWkk4cl34edG7ZNOEUW+3/8eO2rdv3oJmqFFHugdieO+Rp8wLO7ONg0YH4cyoWdaH13RHGLlW+0pfij6qU12e28nJERG/wHfIootd/zffKOjpsYxJ2pUlJy1HyXbH/94vSWUedOmBu65J4zsU0ZMJhsnTxkZMuTC3e4OhWv7/Z3hL0JtU89XLHUK15IVW5ldX9fjOTQea+TYKLiqm4Q4o01koFrNKL+WLZ0whYI4hZICsxmtSsUD+fsYmx6PUq6koPIU4YHRZOXu6tHc6iuD8Y9cRFCEd/IqzlW0rl6K3bJ6kYeqCbrKeR0diRYue8PiiWxTQSYQujAFPBB9XDCgkQ3sobjCNY0OXyGXw/gJhzCbW0JRy8rMBAbK8Pd37Csk7ySU22AU7cerqqz4aS6c5w4xr0uJs7rnj1ol9CxCbVL+QYKUrr83fb4ec62ZphlyCueN7PJcf5mMS+/lQ+RyGi7yzRSue4boqBAigmKJDI4n71zPk12b6vycVqTsEaLI9/vfb1m9xPZs9RIyrz+CsnfkhFyOXPaGxVPZpuqkYPxHu6/Ui8nPxq6UfLaU7sNo7PlWjLeZPv0cBsMR+/OdO5uYMcOJC6PYsh322soqnvmfcoqLW/wooaEyzGYRi0Xk1CkjkyZdcJQeCHy4R2JeHRHqpjJmpWLPDIvSauLqwPZl5x3BVGWi6VQTYVNbbqaeCc/CGN7+xqrkfNXrNLWaPFPbVVqjzUqsssWXKIoimE3EnfoMP381MkGGyeKe76ihSYXFupDoAR0rUnqCcxU5fLzX9dWLekAI/sN7tiK90vGJYWnNNoUWx/sdd9xBZmbXehMXt7kYT2abhlzXD0HTc0d+hdbIp/4HyC7JccOsvM/YsSbMls3253t+bGLyZOciZc6eNXLLLSH85reR3P9AOH//fy2JgosXh/PNNzrmzA1C12Dl2HEjmzY1YLHJ+NIwyK3vA8DPxXL5l1JgCu1xH5F7c6jeWk3Vt1WUvFOCtan7bUXdMR0l75QgItrb5H1fyr/TVSzMy+XFcy0ryiy9nqrz2fnJKhWhcjmH9XqgJUpMLciIVrRoihzUt/hfUhprCan4Ab1ZR1Sw+7aBzCYFuobZHk+kbMP51cv22rXOrV5kEHpDSo+GdkUdsisFyGXLltlfT0xMZPz48T2anzfwqmFxJdu0ozYX48lsU3mgipBZrkeFWBQimWnlfF65G12TY1rzvY3+/QUCAj+zP29utlFTa0Wrdc6p+bulUYSEyM/3qaK62orJJBIfr2TBgmCSkpQolQKnThmIjlbwyYHRFLvJCFyMoHdPYt1ZY89Lp7/1r0yS5mqJnBNJ0Iggzn3auQJoKyVvl2DIM1D9bTUNBxs4tfQUDZkNvP1TJnqFgmMGPUabjSN6PSP8/OztXo6P5/3aWt6rreGtmmp+F3WhunWZ2Ux/lZohGg3GwpMU5q+3J0K6C9Eqo/rcVWgzrnZrv91RUZHHx3uXUxbp2OolYEIcyljntG8upVUd0pkUwYyMDGpqaiguLqagoIAnn3zSnuit1WopLi62P/bv39+j+XkDr9YKS0lJ4amnnuKpp55q83pubq7TbVq57777uO+++9w6z4sJmBRP08FzmMucyy+pj7LwneI4lUXul+b1FsHBAgMGbMZkvrAtcvSoAaVCYMf2FkNpMNjYsb2R1DRVl8amvt5qNywAgYEymhqtqMJbvoIHD+oJCpQRF6skPl7BO5vl0HUxXpcwuiHUGOBkY88Ny/xUOaJfP77RnUAdq6b2++4LOoaODyXm5hgEhYCxzEj5R+Uk/y6ZvJfyuP2vs8hetoMKiwX/S/K+EpQqXorveBVyQ8gFnZbfREZBYxnFKUpOu7kIhYBwXpEy0DuJlK2IIjsPvE90dH9mpNyBUN7xzYU8REXInH5uG9bRfL3ucvIUisuvpONl72PxNHZHvoOIgsiptDo+ad5NZe3la1RkMpgw8TAmc9sM9QkT/Jk9J4irZwZy9cxANBoZV88M7NKoNDXZuOP2QqwXJTk2622EhLYYmpbihmC1gkIp0KwZRInOPQbgUuoa3NPvmWYNoqxn8rQvzdIwV99ioOsP1Nt9Jl0Re1ssgkJo10YRrOAr9Rn00aEcMRiYEdizoIeEj58hLs4zlwevJVJeOm5FHh/veYGyyKIOVy+hP0tF5oatb2fpLidPFEWWLVvGr3/9axYsWNBOg6U3IhkWB1D3C8F/dPcBAvpgG1uSTrO7KBNrN9nQvZ3pMyoxGA53e54otnzxbTaRvzx/jtOn2zt9NRqBoUPV9siw0lIz/fupkMlanh85YmDECA2JWiUNDVZ+NFyHLM49uisXYy0vpbIHOiwXI4oC1oCeB3ck/LSPsn+WYamzEDTKsVWQqdJE0eqiNm1iboqhbmcdzfNDqJfLOaTXs7au1mUhLsFqIf3bPxMc5h5t90vxZiLlpew88EE734vfiCj8Brsmh+BOOsrJO3ToEI8//jirVq1i+fLlLFmyxEezcxzJsDhI6PUDkAV3flde2L+JDcIeCs917Cu6nBgzxozF8k2X51RXW/hkQz1VVRbeeaeWmmorJ08ZqaxsXzpYLhe4/4FwPv2kno0b6/l8YwOP/PrCHn9lhQWtVkVampomvZyvvvwJ/VeOVUy2lBbT/Pk6Km+/rs3rxn27afzPGprWvQeA6fhhAqur0ZvdZ/ANfj03LIOCzNz35Cz8Uvwo/7hldWiq6npVpYpSoV2ibdNGFa0ifGY4VVojtSMTOWrQE6dQsrlR5/LcZDXlDD/5Jkq1Zy4TXk2kvISLVy+yMFWPHfbuoqOcvNWrV9sVXzMyMigtLe31kaWSYXEQmb+S8Jvbl4wxaUR+SC1kc9leDEb33A37kn79BAKDPuv2vIgIBTfdHMLmLQNYvDicyCgF772XxJQpHTs+Bw3ScONNISxcGMKShyMIDb1wt3jtrAt36uN/+z9YtP2xNdQ5NF9FfCL+N7SvEdf8+ToCf/FLzKeOIZqMmE8eo9/A4R304DyGgiM0ZG7i5e9qWPKFnjqD46sCs1Vk6OsXAjm+PmPm3H8ysTZaqdtdR+PJRix1jtX1D5sSRt3uOmymC0EOjcca+TapjMTISJJUSnskmKuoTh1glGm3R3K6wPuJlJey88AHNE23IA/wzMrMGTrLyauqarulHhYW1kY5sjciGRYn0KSHEzDhQgRaZYKJz4IOcrK49+95OkJQsEBK6hZsNt8ZyE+LBiALDELwc7Lwn3DJle+8kRfkCqxVlQh+fgS7yW1Ts3U1QaOv5+Y5k5mXpmDpN45/Xm9nmSlvFGk2i1z/QTOvHzCxZnQDpjwTiNB8thn/1I7fu81oo+DVAsRLRMpES8vz1igkg81C9qhQZAjoeyBo1krgjvfJiPCcCF5rImV4Qj+PjdEZ6ZOnkTbRNzr2l9JRTl5DQwNarbbN1rpOpyMqqnfn2UiGxUlC5g1AiFJzKK2CjdW7aNC1l/a9HBEEkYkTj2Ay+U6PpspvKge/3o5mRs/L88hCwxHNZkSrGf3Xn2GrqyV//btUf7sKm6H70G9D8Unqdr1HQ+Ymar97B3N1yxanPucggjqAhv2fUi6G02wWOVvrWFh0Xq2NUI1AkAqaTCIHSq3oLaAx6wm1aVDFqJAHySl6owh9fvuVhs1oozmvGeG8b8pYZkSdoEbu37L6a85uJiA9AHWcmh0UUp2qRatyz5149PrnSI73nN/Q0KTCZPFuImVAWDjX3P+wx/p3Nl+vo5y8gIAAJk+ebH89JyeHjIwMlyrEe5PePbteiEwtR7w1jp+Kj/l6Km5l+owqDIYsn87hv/tSUU+e7pa+Au57BP3Xn+E3dyGGrzciT0xGhhJN/zHU7X6/y7aiaKN+z1pCp95F8JgFhEy5g9rtbwGgy/qKuLv+iuncWXINAbx72MxjEx3L6Vl/wsytQ1su9FEBMl67TkNVs8jKfUaMhfWETg4lKCOI5txmzDXtSwQoghXE3xVP5ZeVVG+rpmZ7DdolF4IczDVm1HFq/JL9sBqsvCQWcIMbSx313/gMEVGei5qymOToGmaTMMg7CYBzfvlb/AJ7HjZ+Ka7k60HHOXlyuZwVK1awcuVKXn/9dV5//XVWrlzp9jm7m8svQLoXkKjVMn36dL777jtfT8UtjB5jxmrt2lnvaeoNIewvMaEY0c8t/SniE1HccAvW8lLkA9IwnzyKPCIDS9EZLPVdJyLa9Dq4aAdJplQjWlv20cTzOT2ixcTf1qwlwQILB3X/M9qUbeaG9Lbn3TpUydh4OZtzLPxjrprf2pTo8/VEXRdF0MiOL3gh40M6fB0gdHKo/f8xN8ZgA4RDI+Gbg93OzxFkTQ0M2fcKB4c9hr6ph/rOnSBaZVSdm4I2I5CiY9s9MgbA8Gvn0n/UWI/07Uq+HnSekzdu3DjGjfNi1QI3IK1YXGTatGkkJV3+Wg1JyQJBDjjrPc2ms9MwyhUYtn+DYfs3iAY9hu3fYCnM71G/xoN78Z9/I4oELWZNFM2ndhE47Nou28j9Q7AZGtAd/hYAa2MtyoiWlYHMPxTRakaQKwkbOo2bBin4w5auI3R0RpHyRpH0yPZ5GwPCZCwZq2JkiImBQdHoc/UoI5Q0HHTPFutfhuciBLsv6kpZmM3Iyo3IOikq6g5aEilHkjR8QXvfmRuISu7P1b94yO39SlxAMiwuIpPJuOmmm9BovB+H7y4CAwXS0rb61FnfSt74xfjNuQHNzLloZs5F0PihmTkXRVI/l/ts2d9uybwUGhs5u+kNRFMzfmndp/RH3fgMDXvXUf7+k9Tv/Ziwq+8HIHTa3TQe2ULgsGsprDUTHyxn9UETTabO/Sy7Ci2o5LD2mJm1x8w0mVv+n111wWexOcfCeEVLKLEqWkVzTrPL7/tiSuQNHF6Q7pa+WvHb/xUj/LPd2mdHVBSloc1wbyKlys+PBUufQqGSdFY8iWRYekBoaCg33HCDr6fhEoIgMmnyUUwm35fvLwlYyKlLxbxEsSUj32aj7rmnMJ/uRmHTYmlXm8l8JBPV8DHItf0IEmQET7oNZWQydTve7nZOukNfEnnDkwSNnk/TqV3oc1u2k+QBoehzDqAIT8AkU7H/nBK5DNaf6HxraF6akl+MVHF7hpLbM5QEKOH2DKV9BSOKIiIwsDwHpUoJshZHvbt4SXsEob97V9dhm/5Oapx7jF9XVBa7N5Fy9i9/S1hcglv6kugcybD0kCFDhjB16lRfT8Npps+oxmA45OtpAPCd4ib7/63VlTRv+ABbVQVNb7+OrboKy6ljWCs79otYSopo+uDf2KoraXzjb20MkLXiHIqkfijTBqFubKLp1C7Cr3mIxmPbsJk7374yVeYjmvSo49IIGDyNmFv+TM3WNxFtVkSzEWPZGQz5WcjUASj9g0iPkJFVbuWODc1klnYfOSVCGyO4s8DK9GQ5w0NMaM0hWButqKLdd0dtEqxsuM79SYiJG54hJtbzlxB3JVKOnDOf9EmX32/1ckQyLG5g5syZpKe7d7vBk4wabcFq/drX0wBaxbwuOKrlEVH433wnMVszCbzvEeRR0US+/wWaKR1XxVUkaAm48z5ith8i6Fe/RznwQriq36z5iAY9tU//luE//yXhMx9AHng+SsrW+QrDXF1s96kAqGJSkAeGYTM0IvcPIezah9DnZqLPPcjufAOvzNGgM4nsK7ZS3ND5SqNMZ+Pve40UN4j8aYeRkvPnFjXYSI+UMypOjrbBSP3++jaOeHewNuQUhonD3NqnzGRk0NbnCQzxfHJhTxMp4wcOZsY9D7h5VhKdIRkWNyAIAjfddFOvT1oC0CYJBAd/5utp2DkY+DBGD1a5FQ16LNnHUZpaxjBXF6OMTEJQ+VH5+UsYy8+2a6OOG4ip4kIEj2gxIyjVyP1bIrIUAWGETf8FwWMWMCFjAEoZpEfIyX00iIWDOr/IxgXJeHSiGuufgnlupoaE4Jaf313DL6xO3hzbSPKiZJSh7r9YvzqlDpTu7VdeVcKIs++gcIMccHc01fkh87vN6UTKgLBwFjy2DLnC99n1VwqSYXETarWa22+/vVc78wMDBQYO3IbN1rMyH+7ChtwjYl4XIwsNJ+g3T3J83b/R/fQlukNfEbng92C1YCrNxqprX4FaERKN/8Ap1O9bjy7ra+r3fEzErF/Zj1t0VSgjElHFpFBhkPPxcQt3j3DPRSvAqGNycKpb+rqUQ6oySrqRMXYF9fEfGWXb67GyLxdjbFI6lUgpkytYsHQZgWE9U/yUcA5BdEaNRqJbcnJyeO+995wS+fEGgiBy7awTGAw/+XoqdnKCfsGfGr0T/JBxpIGzZa4XZOyMFwcc4bbSFW7tc9PgmTxtaL+ScgcRNn9Wv6VArK5xe9/ltz7LiQrvrNoFuY3wyB8pOdW16NU19/+KkbPneWVOEheQVixuJiUlhVmz3Ku+5w6mT6/pVUYFYJsw12tjuatc/qXkmztPWHSVGXmZKHuo9dIZ1bJm9i4Y4JG+Y9b9GW28d26oLiRSzuz0nJFzrneLUXFFanjHjh2sWrWKV199lSVLlrQpGtnVsb6CtGLxEJ9++imHD3evZ+INRo2yEhj0ga+n0YYGdQaPmP6M+8WH2xMggnVziUf6viaihn81/drt/T4yag476066vV8AOQIffKJFyO46E9wVbIGhHJ7zIrXVnsnM74ho7Zl2ipQpYyey8PGnEdxYUyspKYnCwkKHzs3IyODo0aMIgsDnn3/O1q1b7aVYujrWV5BWLB5iwYIFJCYm+noaJGoFgkM+8/U02rHX70GvGBWAWIvnNv9PNPZMqbEzZhk8d2G2IvLf2Z5JEJQ11pGRuRKNv/fUIS9NpIxLTWf+o0+41aiA41LDAPPmzcNiafkbpqenk5+f79CxvoJkWDyEQqHgzjvv9GmkWECAQHr6Nmw2zyeyOYNV8OPLZu+Vwwnz4M1zmVGFqOpYg6YnXJ2fiULmuVJ+XwSepXH6SI/0rcw/zsjar+wKod6gNZEyMqk/P3vyTyhVaq+N3REvvfQSyvMReOvWrWPx4sUOHesrSIbFg/j7+3P33XcTEuL+ffjuEASYPOU4JpNntoB6wqmgB6gye2u9Aho3ZrF3hNk/tvuTnCREX8cED0WHtfLSuHIEP89EMfrv+ZxhQTke6bszmhvj+dkTK/AP9v7vrSPy8vJYtGgRZWVlLFy40OFjfQHJsHiY4OBg7r77bvz9nRSu6iHTptdgMHSu/eBLvrVN8+6AzZ7d72/WeEb9cI6HDeIpZRU5892jqtkRERv/Rkq8d+rQqfwU3PDbEYREu3/16Cr9+/fn/fffZ8KECfzhD39w+FhfQDIsXiAyMpK77roLtdo7y/ORI63YbF96ZSxnqfKbSmaTd792xqb22ibupF7hme3OmfmZKATPKlv8JeU4QqznZIGT1v8P0TGe9bcolDLmPzKcyET3a6u4g3vuuYd33nkHfQcy0V0du5yRDIuXiI+PZ9GiRfa9VU+RmCgjJPQzj47RE3ar7/b6mPUNXZe17ylVQoRH+g1prmV8SIpH+m5FJzOy7XrPFWUUTAYG73iBgGDPGEiZTGDOQxnEp4Z6pH9XaG5u5vrrr8dma7viNJlMXR7rS0iGxYskJSVx5513olB45kcWECCQPmh7r3PWt2KWhfN1Y6TXx63wUA5LK6Wi57K6Z5k8nw2wOuoo1uGeq3UnryhkZP77KJTujtISuPa+IfQb5p3vlKNSw01NTRw4cMAeRZadnU1GRgYhISFdHutLSIbFy/Tv35/bbrutnbZ1T2lx1p/EZCpya7/u5HDQwzRavZs2FWkDg4cDBQo8kCTZyjX5h5ALng/d/edMK3hQR119ZCcjBfcoWQLI5AKzHxhK2tgYt/XZGc5KDUdFRfHaa6/x4osv8vrrr/PGG2/w/vvvd3usLyElSPqI7Oxs1q1bZ49n7ynTp9diE79wS1+e4jn/T9rrrniYISYZuTs8a2xvjKng1frfeaz/B0Zdy7660x7rv5U1mSMI2+zZgI+y257j5LmerfBkCoG5D2bQf0TvL/p6pSKtWHxEenq62xz6I0ZYsYm901nfSmnAAq8bFYAgs+fHPNnkmSTJVmabvJMP8sKIAoQgz76X2HXPkhjvenu5QsZ1vxwmGZVeTp81LLW1tdx77708++yz/Pa3v8Vg6H6ffcqUKYSGhpKYmEhiYiIhISEUFbXc7S5btsz+emJiIuPHj+/xHPv168e9995LQIDrIZIJCTLCwj6jRT6q97JD8XOfjKsydC+81VPONGkQPVTbC+CagiyvbIcVKOo4fv1gj44h2KykfvEnQiOc/7wUajnzHxnuNZ+KhOv0WcPy+OOPc/vtt/Pss89y9dVX89xzz3XbJiMjg5qaGoqLiykoKODJJ59Eq20RfNJqtRQXF9sf+/d3XVXVUeLi4rjvvvsIDQ11uq2/v8Dgwdux9lJnfSuXinl5E5uHc1gArKIMa4DnQnYjGisZHeyZwpGXsjz5CEKSZ6V7ZQ3VDMt6HZWf48ZSHaBg4e9Goh0slb+/HOiThqWxsZGtW7cye/ZsAObOncvbb7/dLsTvYsxmM0uXLrVHa3zwwQfccsst9uOeiuQCiIiI4L777nOy/IvIlKtOYezFzvpWPC3m1RXNOu+EcRo0nnUiz7Z4p/aWUbDy+TzPX7yVOVmM0m1BcOAK5B+i4sbHRhPbv29FTvVl+qRhOXz4MOnp6XYj4efnR3BwMLm5nVdzVSqVDBrUIjpls9nIzMwkLS3NflwURZYtW8avf/1rFixYwJkzZ9w65+DgYO677z6HC1dOm16PweCeVZMn8YaYV1fUeDiHpRWdyrN7/tcWHEbmyFXYDfw37CSm8RkeHydg9waGhXRdLTg4yo+bnxhDRIJnfT8S7qVPGpaysjIiItomrYWFhVFRUeFQ+40bNzJp0qQ2rx06dIjHH3+cVatWsXz5cpYsWeK2+bbi5+fHPffcQ0pK10lxw4fbEHt5BFgr+UGLKPJwaZLOUIpQ2eCdkiLVMs/u+0fqzjEq2LPJkhez8iodeHCV3krkpy/SP77jVWVM/2BufmIMwZF+Hp+HhHvpk4bFZDK1U3C02WwOywavWbPGvo3WyurVq4mMbLl4ZGRkUFpaitHo/rthlUrFnXfeyfDhHddwio+XER7xGb3dWd/KVuE6n40daxOweeljKhfDPD7GLKvnL/St7FeXUD53lFfG6rfhaaKi2763tHEx3PjYaPyDPVPeX8Kz9EnDEhMTQ01NW+nVuro6YmO7r0JbXV1NXl4eYWFtLxRVVW210cPCwjym/CaXy7npppuYNWsWgnAh1NTPX2DI0O+wWps8Mq67aVBnsEvnuwtDuNl7ZduLrF4wLIVHELwhLH+e54eeRggL9fg4glHPkF0r8A9SgAATbujP7PuHIndDpn5WVhavvPIKr732Gg899BCHDh3qto0rEaUSbemThmX06NEcP37cvmppamrCZrMRFxfXbdsff/yR6Oi2ET4NDQ1otVqs1guhqzqdzuNaK1OmTOHOO+88v9ISueqqbIzGAo+O6U68KebVEYEm742eZ/S8Yzm6voyRHq4ddjEVsiYOLPBs6f5W5GV5jKrcyJwHMhg7r7/b+n366ad57LHH+M1vfsPzzz/Pww8/3G0bVyJKJdrSJw1LWFgYM2fOZMeOHQB8/fXX3HPPPYii2Gl9n1bOnDnTrtxKQEAAkydPtr+ek5NDRkaGU4pyrpKWlsYDDzzAtbPkGAz7PD6eu/C2mFdHyPSez2Fp5VSzd8q1z7J6dwX4t4TDkNbP4+MotVoGLXuI1DHuC9uura1l586d9srB0dHR7XYeLsWViFKJ9nhv09bLrFq1iieeeIIffviBiooKXn755Tb1fcaMGdNhu9DQ0HZbZnK5nBUrVrBy5UoUCgU5OTle1aiOjIxkYuhjnDh5jsrKb7w2bk/IDnqAKp1vf4zmZs+Wy7+Y442BXvk1zSo6yl/DFIhe8rFZEflwjj93uDcIsg0BU6eS8PJfkbu5EGNYWBhffvml3bdaU1PT7S5DVxGlqaneWb31BaRaYZcZBYVvkpPzV0TRe3fjrvBK4Dqv665cSsaRBs6W6bw2Xm7Yb5Dpqz0+zqIRMzjS0HnovCd4Z9dQ/Hcfdm+ngkDEQw8R9ehv3a5P3xF//OMfmTp1arvAnItZv34969evZ+3atfbXJk2axN/+9jcmT57s8Tn2FfrkVlhfJjnpQUaN/C8qVe8ta+ELMa+OqKzzrtPV6AGJ4o6YLXo//PavEyoR3ChUJw8JIfG1lUQv/Z1XjMqWLVtISUnp0qhAzyNKJVrw/a9fwmnCwiYwfvyXREZe6+updIgvxLwuJdAG9XrvbYUBNKm9UxhxdtExr4xzMcdVFeTPH+GWvvwnTqT/5xsJutY739/MzEyKi4u59957uz23JxGlEheQDMtliloVyYjhaxg8aAVyee/JSjbLwvmm0feVZ2Nt3gvLbaVW7p1VZFxtEcO8VDvsYp4feAIhxvW/raBUEv3E70l6+98oYzyvowJQUFDA999/z+LFiwHIy8vrUga4JxGlEheQDMtlTnz8LUwY/yWhoRN8PRUAjgQtQWf1fQRNmHcXKwBU4BmJ4o6YJXp/a6ZeMPD99VqX2qpSUuj30Voi7r+/TW6WJzGbzfzzn//kd7/7nf21NWvWoFarO40O7Syi1Ftz7itIhqUP4OeXyOhR75GaugyZzLeZyl+Zx/p0/FbUBu8btxJrqNfGmlV8wmtjXcyq6CPYMgY63kAmI3zxYvp/sgHNkCGem1gHbN68mdWrV5OUlERiYiIxMTFs2LChS/VHaIko/fDDD3nuuef4/vvv+cMf/uDVefcFpKiwPkZj42lOnPg9usbjXh+7NGABTzTf6/VxO2JqqZkDRx2rDecuHtbm82Tl014b77bh0zihy/faeK1c29yfh1aehW4uHarkZOKWL8d/tHdKw0j0HqQVSx8jMHAgY8duoF/yrxC8IA51Md/5SMyrI/SN3t8LO6sP9up4s/BOUualbPXPo25mF8ZCJiP8F/fQf+NnklG5QpEMSx9EJlOSkvI448Z9TkhIx4mg7kavSGKrj8S8OqLeS+XyL+Z4o3cv9HNKTnp1vItZMboIoQPlU/WQwSS//x4xy5Yhk0J0r1gkw9KHCQocxJjRHzF48IsolZ51LGcGLvGZmNelCKJIRb33CweWGtSISu8ZF211PoOCkr023sXkKmo5teCCz0QWEkLMn/5I//Xr8R8lrVKudCTD0scRBIH4uJ8zaeJWEhPu9sj2WIuYl2e10p0hSpRhtPgmMs0c4J0w2lZmC75bJf6l31GEpERCb/k5Kd98Tfidd3ol2VGi9yN9C64QlMpg0tOfZfy4LwgPu8qtfecHLaLQR2JeHRFl9V1oaLOHJYovZXbJKa+OdzGDY4ahePf/EffccyjCPC8bIHH5IBmWK4zAwIGMGvUfhg//J35+/dzS5zYfinl1RJAPjVy9wrvJoclVuQwM9G4V6YTABF6e/jLvXvcuqbFD3d5/bm4uq1evJjnZ8W2+rnRXli1bRmJiov0xfvx4t89Zoi19trqxRNdERV5DRPg0Sks/Ir/gDYzGcpf6aVBnsNOHYl4dofShYakSwvG212OWLJjTXhgn0i+SB4Y9wC0Db0El99zffMCAASxZsoQXXnjB4TZPP/00X331FQAVFRXccMMN7N27FwCtVttpzoqEZ5AMyxWMTKYkMfEu4uNvpbR03XkDU+ZUH3v9HsTWsWS5z7A2+SDt/jxlYrjXx5xddpp/eDBmIFwTzuKhi7l90O1oFN6L9HJU7+hi3RU/P792uisKhXSZ8zbSJy6BTKYiMXER8fG3UFq2noL8NzAYS7ttZxU0fNWcBD7ViWxPsw9yWFopMHteSfJSBlScJXXYFM42Frm132BVMIszFnPnoDvxV/q7tW930p3uiiiKLFu2DJ1OR0FBAa+88gppaWm+mu4VgWRYJOzIZCoSE+4kPu4WysrWk1/wBgZDSafnnwp6gEofi3l1RI0PQo1byTF437AAzJKFcBb3GJYwdRh3DL6DuwffTaCq9xQ47Yrp06fb///qq6/y5z//2f780KFDPP/880RGRnLs2DGWLFnCtm3bfDHNKwbJsEi0QyZTkpBwB3FxP6es/BPy89/AYGh/0dpsm95Ba9+iEqFK5/3kyFZONvnmQjy7PIc3erioSAxM5J6h93Bj6o1e3fJyJx3prqxevdr+/4yMDEpLSzEajajdqC8j0RbJsHiJ2tpali5dSr9+/aipqeGll17qVjxIFEVef/11bDYbR44cYeLEidx///0ATJkyhePHjxMY2HIh0+l0HDt2DK3WteqzHSGTKUmIv434uFuoqtpGUfG71Nb+CEC13xQO9gIxr0uJtQlU+DBP83STBtFPgWCzeHXc1HPZDBg2mdxG553Uo6NHc9eQu5ipnYlc5t0yQO6kVXeltUR+K1VVVURGXpA0CAsLo66ujhgvle6/EpEMi5d4/PHHuf3225k7dy6ffvopzz33HH/5y1+6bPPqq68yf/580tPTOXr0KDt37rQfy8jIYNeuXchkMqxWKy+++KJbjcrFCIKMqKhZREXNorHpDMXF/+WL5qvBdztOnRJhAe+WnmyLVZRhDYhBoet8C9FTzJKHsQbHDItGrmFW8iwWDVnE0Aj3hwx7m1bdlcceewxo0V2JjY3FbDaj1WppbGxELm8xmjqdro0PRsL99L5bzj5IY2MjW7dutS/P586dy9tvv43N1rl/wmKx8O2335Keng7AsGHDeOSRR4AWnYmlS5fao2Y++OADbrnlFg+/ixYCA9IYlP5/PDdsGi8OTGRYoPdlcrvCz+j7sjIGTbRPxp1VltPtOYPDB/PMhGfYfut2Xpj6Qq82Kmaz2S64ZbPZOtVQ6Up3JSAggMmTJ9uNSk5ODhkZGQ5HnEm4hrRi8QKHDx8mPT3d/mX28/MjODiY3NxcUlNTO2yTlZVFcHAwy5cvJzc3l7y8PJ555hlmzJiBUqlk0KBBQMsPLjMzk7vv9q4ccKBCzi8SIvlFQiRZDc28X1bNpoo66ixWr87jUuR6725BdYROFYUvPC3p507RL2Mi+U1tI/qCVcHMHzCfm9JuYlD4IB/MzDlycnJYt24dpaWlPP744yxatIihQ4faNVTGjGlbWLVVd+U///kP0GJogoODWbFiBQArVqxg5cqVKBQKcnJyWLlypdff05WGZFi8QFlZGRERbYtAhoWFUVFR0alhyc/PZ8+ePSxdupRly5ZRVVXFrFmz+Omnn9qo2W3cuJFJkyZ5dP7dMTLYn5HB/ryQlsh3NQ18XlnHt1X1NPigXpe5yfeGpUYWia+EbGcpIniTUpQyJVMSpnBdv+u4Jvka1PLLx1GdkpLCU089xVNPPdXm9dzc3A7Pnz9/PtXV1Z32N27cOMaNG+fWOUp0jWRYvIDJZOJSPTWbzdal816v1zN8+HAmT54MQGRkJFqtljNnzjBw4AUFvzVr1vDhhx96ZuJOopQJzIoMYVZkCEabje9qdHxe0WJkGr0kV6zzYURYK+WE45MNJrmaGxSRJE95nplJMwlS9R4ZA4krC8mweIGYmBhqamravFZXV0dsbGynbYKCguz7wq1ERERQV1dnf15dXU1eXh5hvbAAoFomY05kCHMiQzBYbWyvaeDzijq21zR4dCVT6cMcllaKLF78e6hDIOVqGLwA0mbTTxNMP++NLiHRIZJh8QKjR4/m+PHjiKKIIAg0NTVhs9mIi+t8wyQjI4Ps7Ow2r9XX15OYmGh//uOPPxId7RtHsTNo5DLmRYUyLyoUqyjyU0MzO2oa+K5GR1ZDs9vy9oNFgYZe4GPJM3pQSVKQQ8IYSJnZ8kgYA3LpZyzRu5C+kV4gLCyMmTNnsmPHDmbOnMnXX3/NPffcgyiK3Hnnnfz+979v55BMTU0lIiKCvXv3MnHiRCwWCxqNhvj4ePs5Z86cabeq6e3IBYFxIQGMCwngD/3jqDNb+LGukR9qG9ld10h2k+srjhir4Kbc856R3exm1334ABgwo8WQ9J8GGt9k90tIOIpkWLzEqlWreOKJJ/jhhx+oqKjg5ZdfxmQydRrpAvDhhx/yzDPPcODAARoaGtpVew0NDe1yO+1yIFSpsK9mACpNZg7UN3FEp+eIrpkjOj1VZsdWIaFmsVcYluONAa7/slSBEDcCEseCdgIkjofA3pFzkZuby+bNm1m+fDkFBQUOtcnKymL79u0olUqOHj3Kww8/zChJYbLPI4iXepUlJHoZZUZTG0NzVKen3NS+0ORVtSIH93dfPNMb5Ib9Gpm+puuTghMgKh0i0yF2GCSMbvl/L8+xSEpKorCw0KFz582b12k5e4m+i7Rikej1xKlVxKlVzIm8sAVUaTJzttlInt5IfrORPL0JTVMjof5K6pp9V924FZN/LBp9TYtzPSQBwvpB5ECIGgRRA1v+r748o7bcVc5eou8iGRaJy5IolZIolZJJoRf5MzKA6zNoMloordNTXKenutFEXbOJer2ZumYztRf9v05vor7ZjNkqYrbasNg6X7yr5DL81XICVAoC1HL8L/o3UK0gzF9FVJCa6CA10cFqrCEfQnA4aDzoyO/ldFfOXqLvIm2FSUhchNlqw3qJgZHLBJTy3r095U369etHfn6+0+3++Mc/MnXq1DaVhyX6JtKKRULiIpRyGcrLK9DusqCjcvYSfRfpNkxCQsKjtJazv/fee309FQkvIRkWCQkJj9Fazr5VIyUvLw+9Xu/jWUl4GmkrTAJwTYjsxx9/5JtvviEqKoqSkhIWL15sL/Pf1TGJy5vWcvaCIGCz2Vi0aFGHSb6t5eyfe+45+2tr1qxpl48l0QcRJSREUVy8eLH49ddfi6Ioip988on49NNPd3m+1WoVr7vuOvvz5uZmcd68ed0ek7h8OXv2rLh8+XIREJcuXSoePHhQ1Ov1Yv/+/cXPPvus3flffPGFGB4eLiYkJIgJCQlidHS0mJqa6oOZS3gbybBIiDqdTtRqtaLVahVFscUQxMXF2Z93REVFhTh37tw2r82cObPbYxKukZOTI77xxhtiUlKS021NJpM4ZMgQD8xKQqJjJB+LRJdCZJ0RFRVFdXU1b731FgDl5eUMHjy422MSrjFgwACWLFnSTn7BEd5++23Ky8s9MCsJiY6RfCwSLgmRAXz22WdMnTqVd999l1GjRvHyyy87dEzCdZyV1M3LyyM0NJSgoMszy1/i8kRasUi4JEQG8I9//IOPPvqIRx55hI8++oivv/7aoWMS3mP9+vXceuutvp6GxBWGZFgkXBIiO3r0KDqdjrFjx3Lbbbfx9ddf8+ijj2K1Wrs85glyc3NZvXo1ycnJDrdZtmwZiYmJ9sf48eM9MjdfsmnTJm644QZfT0PiCkQyLBJthMgAh4TITp06xZAhQ+zPR40aRVxcHLW1tV0e8wSu+B+0Wi3FxcX2x/79+z0yN1+h0+koLy+XQrwlfIJkWCTaCJEBbYTI7rjjDjIzM9u1GT9+PFlZWfbnRqMRf39/IiMjuzzmSZzxPygUfdu9uGvXLlQqFWvXrmXt2rU0NTWxdu3adqqkEhKeQCpCKQG0bH098cQTJCUl2YXIRFFkyJAhvPrqqyxcuLBdmy1btnDo0CFCQkIoLS3l9ttvt0d/dXXMUzhTHHHNmjXk5+ej0+koKCjglVdeIS0tzaPzcweuFoB0tZ2EhCtIhkWiz+DMxXPJkiU8//zzREZGcuzYMR599FG2bdvm2Qm6gYSEBIqLi7vNer+U5ORk8vPzEQTBSzOVuJKRtsIkrkhWr15t35rLyMigtLQUo9Ho41l1Tk5ODitWrKC0tJTHH3+czMzMNtLWnVFWVsbf//53iouL+dOf/kRJSYkXZy1xpSKtWCT6DM6sWKqqqtr4fCZPnsynn35KTEyMh2YnIXHlIK1YJK44Ghoa0Gq1bcKfdTqdpG4oIeEmJMMi0WdorboLLQmenUW0BQQEMHnyZOTyFkWvnJwcMjIynM5ql5CQ6Ji+HXMpcUWQk5PDunXr7P6HRYsWMXToULv/4VLHtlwuZ8WKFaxcuRKFQkFOTg4rV6700ewlJPoeko9FQkJCQsKtSGt/CQkJCQm3IhkWCQkJCQm3IhkWCQkJCQm3IhkWCQkJCQm3IhkWCQkJCQm3IhkWCQkJCQm3IhkWCQkJCQm3IhkWCQkJCQm3IhkWCQkJCQm3IhkWCQkJCQm3IhkWCQkJCQm3IhkWCQkJCQm3IhkWCQkJCQm3IhkWCQkJCQm3IhkWCQkJCQm3IhkWCQkJCQm3IhkWCQkJCQm3IhkWCQkJCQm3IhkWCQkJCQm3IhkWCQkJCQm3IhkWCQkJCQm3IhkWCQkJCQm3IhkWCQkJCQm3IhkWCQkJCQm3IhkWCQkJCQm3IhkWCQkJCQm38v8BYUSYvNFLGF4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制饼图\n",
    "plt.pie(df2['登录间隔时长用户数量'], labels=df2['登录间隔时长'], autopct='%1.1f%%')\n",
    "plt.title('登录间隔时长占比')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e5095248",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>距离期末天数</th>\n",
       "      <th>登录用户数量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>84</td>\n",
       "      <td>5004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>2807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>2599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>374</td>\n",
       "      <td>2491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22</td>\n",
       "      <td>2284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>474</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>2515</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>1620</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>2383</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>496</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>335 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     距离期末天数  登录用户数量\n",
       "0        84    5004\n",
       "1        21    2807\n",
       "2        20    2599\n",
       "3       374    2491\n",
       "4        22    2284\n",
       "..      ...     ...\n",
       "330     474       1\n",
       "331    2515       1\n",
       "332    1620       1\n",
       "333    2383       1\n",
       "334     496       1\n",
       "\n",
       "[335 rows x 2 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_day= user1['distance_day'].value_counts() # 统计用户登录天数\n",
    "\n",
    "df3 = pd.DataFrame({'距离期末天数': user_day.index, '登录用户数量': user_day.values})  \n",
    "\n",
    "df3.to_csv('login_day.csv', header=True, index_label='序号')\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0c8100b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
